{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 案例数据背景介绍"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.数据来源：\n",
    "\n",
    "     Dongjin Cho，韩国蔚山国立科技学院城市与环境工程学院\n",
    "\n",
    "     Cheolhee Yoo，韩国蔚山国立科技学院城市与环境工程学院\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.摘要：\n",
    "\n",
    "    本案例所使用的数据集包含了韩国首尔2013至2017年夏天的14个天气预报（NWP）的气象预报数据、2个现场观测数据和5个地理辅助变量。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.数据集信息：\n",
    "\n",
    "    这些数据是为了修正韩国气象局在首尔运行的LDAPS(Local Data Assimilation and Prediction System)模式下次日最高和最低气温预报的偏差。该数据包括2013年至2017年的夏季数据。输入数据主要由LDAPS模型的次日预报数据、当前的现场最高和最低气温以及地理辅助变量组成。此数据有两个输出数据（即第二天的最高和最低气温）；2015年至2017年期间进行了后期验证。\n",
    "    关于LDAPS模式: http://qikan.camscma.cn/jamsweb/cn/article/doi/10.11898/1001-7313.20200103?viewType=HTML\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.数据属性介绍：\n",
    "\n",
    "      1. station - 气象站编号: 1 - 25 \n",
    "      \n",
    "      2. Date - 记录日期: yyyy-mm-dd ('2013-06-30' to '2017-08-30') \n",
    "      \n",
    "      3. Present_Tmax - 记录日期的0-21时最高气温(Â°C): 20 - 37.6 \n",
    "      \n",
    "      4. Present_Tmin - 记录日期的0-21时最低气温(Â°C): 11.3 - 29.9 \n",
    "      \n",
    "      5. LDAPS_RHmin - LDAPSM模式预测的次日最小相对湿度 (%): 19.8 - 98.5 \n",
    "      \n",
    "      6. LDAPS_RHmax - LDAPS模式预测的次日最大相对湿度 (%): 58.9 - 100 \n",
    "      \n",
    "      7. LDAPS_Tmax_lapse - LDAPS模式预测的次日最高气温递减率 (Â°C): 17.6 - 38.5 \n",
    "      \n",
    "      8. LDAPS_Tmin_lapse - LDAPS模式预测的次日最高气温递减率 (Â°C): 14.3 - 29.6 \n",
    "      \n",
    "      9. LDAPS_WS - LDAPS 模式预测的次日平均风速 (m/s): 2.9 to 21.9 \n",
    "      \n",
    "      10. LDAPS_LH - LDAPS模式预测的次日平均潜热通量 (W/m2): -13.6 - 213.4 \n",
    "      \n",
    "      11. LDAPS_CC1 - LDAPS模式预测的次日第一个6小时平均云量 (0-5 h) (%): 0 - 0.97 \n",
    "      \n",
    "      12. LDAPS_CC2 - LDAPS模式预测的次日第二个6小时平均云量 (0-5 h) (6-11 h) (%): 0 to 0.97 \n",
    "      \n",
    "      13. LDAPS_CC3 - LDAPS模式预测的次日第三个6小时平均云量 (0-5 h) (12-17 h) (%): 0 to 0.98 \n",
    "      \n",
    "      14. LDAPS_CC4 - LDAPS模式预测的次日第四个6小时平均云量 (0-5 h) (18-23 h) (%): 0 to 0.97 \n",
    "      \n",
    "      15. LDAPS_PPT1 - LDAPS模式预测的次日第一个6小时平均降水量 (0-5 h) (%): 0 to 23.7 \n",
    "      \n",
    "      16. LDAPS_PPT2 - LDAPS模式预测的次日第二个6小时平均降水量  (6-11 h) (%): 0 to 21.6 \n",
    "      \n",
    "      17. LDAPS_PPT3 - LDAPS模式预测的次日第三个6小时平均降水量 (12-17 h) (%): 0 to 15.8 \n",
    "      \n",
    "      18. LDAPS_PPT4 - LDAPS模式预测的次日第四个6小时平均降水量  (18-23 h) (%): 0 to 16.7 \n",
    "      \n",
    "      19. lat - 维度 (Â°): 37.456 - 37.645 \n",
    "      \n",
    "      20. lon - 经度 (Â°): 126.826 - 127.135 \n",
    "      \n",
    "      21. DEM - 高程 (m): 12.4 - 212.3 \n",
    "      \n",
    "      22. Slope - 坡度 (Â°): 0.1 - 5.2 \n",
    "      \n",
    "      23. Solar radiation - 太阳辐射量 (wh/m2): 4329.5 to 5992.9 \n",
    "      \n",
    "      24. Next_Tmax - 次日最高气温 (Â°C): 17.4 to 38.9 \n",
    "      \n",
    "      25. Next_Tmin - 次日最低气温 (Â°C): 11.3 to 29.8\n",
    "      \n",
    "\n"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5. sklearn中常见的几个线性回归类库：**\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:21:36.589506Z",
     "start_time": "2020-06-16T01:21:34.243221Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "#import pandas_profiling as pp \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#全部行都能输出\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:21:36.939352Z",
     "start_time": "2020-06-16T01:21:36.858414Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>Date</th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>Present_Tmin</th>\n",
       "      <th>LDAPS_RHmin</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_Tmin_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>...</th>\n",
       "      <th>LDAPS_PPT2</th>\n",
       "      <th>LDAPS_PPT3</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "      <th>Next_Tmin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>28.7</td>\n",
       "      <td>21.4</td>\n",
       "      <td>58.255688</td>\n",
       "      <td>91.116364</td>\n",
       "      <td>28.074101</td>\n",
       "      <td>23.006936</td>\n",
       "      <td>6.818887</td>\n",
       "      <td>69.451805</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.7850</td>\n",
       "      <td>5992.895996</td>\n",
       "      <td>29.1</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>31.9</td>\n",
       "      <td>21.6</td>\n",
       "      <td>52.263397</td>\n",
       "      <td>90.604721</td>\n",
       "      <td>29.850689</td>\n",
       "      <td>24.035009</td>\n",
       "      <td>5.691890</td>\n",
       "      <td>51.937448</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>127.032</td>\n",
       "      <td>44.7624</td>\n",
       "      <td>0.5141</td>\n",
       "      <td>5869.312500</td>\n",
       "      <td>30.5</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>31.6</td>\n",
       "      <td>23.3</td>\n",
       "      <td>48.690479</td>\n",
       "      <td>83.973587</td>\n",
       "      <td>30.091292</td>\n",
       "      <td>24.565633</td>\n",
       "      <td>6.138224</td>\n",
       "      <td>20.573050</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5776</td>\n",
       "      <td>127.058</td>\n",
       "      <td>33.3068</td>\n",
       "      <td>0.2661</td>\n",
       "      <td>5863.555664</td>\n",
       "      <td>31.1</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>32.0</td>\n",
       "      <td>23.4</td>\n",
       "      <td>58.239788</td>\n",
       "      <td>96.483688</td>\n",
       "      <td>29.704629</td>\n",
       "      <td>23.326177</td>\n",
       "      <td>5.650050</td>\n",
       "      <td>65.727144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6450</td>\n",
       "      <td>127.022</td>\n",
       "      <td>45.7160</td>\n",
       "      <td>2.5348</td>\n",
       "      <td>5856.964844</td>\n",
       "      <td>31.7</td>\n",
       "      <td>24.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>31.4</td>\n",
       "      <td>21.9</td>\n",
       "      <td>56.174095</td>\n",
       "      <td>90.155128</td>\n",
       "      <td>29.113934</td>\n",
       "      <td>23.486480</td>\n",
       "      <td>5.735004</td>\n",
       "      <td>107.965535</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>127.135</td>\n",
       "      <td>35.0380</td>\n",
       "      <td>0.5055</td>\n",
       "      <td>5859.552246</td>\n",
       "      <td>31.2</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   station       Date  Present_Tmax  Present_Tmin  LDAPS_RHmin  LDAPS_RHmax  \\\n",
       "0      1.0  6/30/2013          28.7          21.4    58.255688    91.116364   \n",
       "1      2.0  6/30/2013          31.9          21.6    52.263397    90.604721   \n",
       "2      3.0  6/30/2013          31.6          23.3    48.690479    83.973587   \n",
       "3      4.0  6/30/2013          32.0          23.4    58.239788    96.483688   \n",
       "4      5.0  6/30/2013          31.4          21.9    56.174095    90.155128   \n",
       "\n",
       "   LDAPS_Tmax_lapse  LDAPS_Tmin_lapse  LDAPS_WS    LDAPS_LH  ...  LDAPS_PPT2  \\\n",
       "0         28.074101         23.006936  6.818887   69.451805  ...         0.0   \n",
       "1         29.850689         24.035009  5.691890   51.937448  ...         0.0   \n",
       "2         30.091292         24.565633  6.138224   20.573050  ...         0.0   \n",
       "3         29.704629         23.326177  5.650050   65.727144  ...         0.0   \n",
       "4         29.113934         23.486480  5.735004  107.965535  ...         0.0   \n",
       "\n",
       "   LDAPS_PPT3  LDAPS_PPT4      lat      lon       DEM   Slope  \\\n",
       "0         0.0         0.0  37.6046  126.991  212.3350  2.7850   \n",
       "1         0.0         0.0  37.6046  127.032   44.7624  0.5141   \n",
       "2         0.0         0.0  37.5776  127.058   33.3068  0.2661   \n",
       "3         0.0         0.0  37.6450  127.022   45.7160  2.5348   \n",
       "4         0.0         0.0  37.5507  127.135   35.0380  0.5055   \n",
       "\n",
       "   Solar radiation  Next_Tmax  Next_Tmin  \n",
       "0      5992.895996       29.1       21.2  \n",
       "1      5869.312500       30.5       22.5  \n",
       "2      5863.555664       31.1       23.9  \n",
       "3      5856.964844       31.7       24.3  \n",
       "4      5859.552246       31.2       22.5  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data = pd.read_csv(r\"D:\\360MoveData\\Users\\ASUS\\Desktop\\拉钩数据分析\\模块8\\模块三资料\\阶段三\\Dataset.csv\")\n",
    "Data.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据探索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 基本统计信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:45.892284Z",
     "start_time": "2020-06-16T00:12:45.810809Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>station</th>\n",
       "      <td>7750.0</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>7.211568</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Present_Tmax</th>\n",
       "      <td>7682.0</td>\n",
       "      <td>29.768211</td>\n",
       "      <td>2.969999</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>27.800000</td>\n",
       "      <td>29.900000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>37.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Present_Tmin</th>\n",
       "      <td>7682.0</td>\n",
       "      <td>23.225059</td>\n",
       "      <td>2.413961</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>21.700000</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>24.900000</td>\n",
       "      <td>29.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_RHmin</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>56.759372</td>\n",
       "      <td>14.668111</td>\n",
       "      <td>19.794666</td>\n",
       "      <td>45.963543</td>\n",
       "      <td>55.039024</td>\n",
       "      <td>67.190056</td>\n",
       "      <td>98.524734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>88.374804</td>\n",
       "      <td>7.192004</td>\n",
       "      <td>58.936283</td>\n",
       "      <td>84.222862</td>\n",
       "      <td>89.793480</td>\n",
       "      <td>93.743629</td>\n",
       "      <td>100.000153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>29.613447</td>\n",
       "      <td>2.947191</td>\n",
       "      <td>17.624954</td>\n",
       "      <td>27.673499</td>\n",
       "      <td>29.703426</td>\n",
       "      <td>31.710450</td>\n",
       "      <td>38.542255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_Tmin_lapse</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>23.512589</td>\n",
       "      <td>2.345347</td>\n",
       "      <td>14.272646</td>\n",
       "      <td>22.089739</td>\n",
       "      <td>23.760199</td>\n",
       "      <td>25.152909</td>\n",
       "      <td>29.619342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>7.097875</td>\n",
       "      <td>2.183836</td>\n",
       "      <td>2.882580</td>\n",
       "      <td>5.678705</td>\n",
       "      <td>6.547470</td>\n",
       "      <td>8.032276</td>\n",
       "      <td>21.857621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>62.505019</td>\n",
       "      <td>33.730589</td>\n",
       "      <td>-13.603212</td>\n",
       "      <td>37.266753</td>\n",
       "      <td>56.865482</td>\n",
       "      <td>84.223616</td>\n",
       "      <td>213.414006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.368774</td>\n",
       "      <td>0.262458</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.146654</td>\n",
       "      <td>0.315697</td>\n",
       "      <td>0.575489</td>\n",
       "      <td>0.967277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.356080</td>\n",
       "      <td>0.258061</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.140615</td>\n",
       "      <td>0.312421</td>\n",
       "      <td>0.558694</td>\n",
       "      <td>0.968353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.318404</td>\n",
       "      <td>0.250362</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.101388</td>\n",
       "      <td>0.262555</td>\n",
       "      <td>0.496703</td>\n",
       "      <td>0.983789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.299191</td>\n",
       "      <td>0.254348</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.081532</td>\n",
       "      <td>0.227664</td>\n",
       "      <td>0.499489</td>\n",
       "      <td>0.974710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.591995</td>\n",
       "      <td>1.945768</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052525</td>\n",
       "      <td>23.701544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT2</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.485003</td>\n",
       "      <td>1.762807</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018364</td>\n",
       "      <td>21.621661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT3</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.278200</td>\n",
       "      <td>1.161809</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007896</td>\n",
       "      <td>15.841235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.269407</td>\n",
       "      <td>1.206214</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>16.655469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>37.544722</td>\n",
       "      <td>0.050352</td>\n",
       "      <td>37.456200</td>\n",
       "      <td>37.510200</td>\n",
       "      <td>37.550700</td>\n",
       "      <td>37.577600</td>\n",
       "      <td>37.645000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lon</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>126.991397</td>\n",
       "      <td>0.079435</td>\n",
       "      <td>126.826000</td>\n",
       "      <td>126.937000</td>\n",
       "      <td>126.995000</td>\n",
       "      <td>127.042000</td>\n",
       "      <td>127.135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEM</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>61.867972</td>\n",
       "      <td>54.279780</td>\n",
       "      <td>12.370000</td>\n",
       "      <td>28.700000</td>\n",
       "      <td>45.716000</td>\n",
       "      <td>59.832400</td>\n",
       "      <td>212.335000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slope</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>1.257048</td>\n",
       "      <td>1.370444</td>\n",
       "      <td>0.098475</td>\n",
       "      <td>0.271300</td>\n",
       "      <td>0.618000</td>\n",
       "      <td>1.767800</td>\n",
       "      <td>5.178230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solar radiation</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>5341.502803</td>\n",
       "      <td>429.158867</td>\n",
       "      <td>4329.520508</td>\n",
       "      <td>4999.018555</td>\n",
       "      <td>5436.345215</td>\n",
       "      <td>5728.316406</td>\n",
       "      <td>5992.895996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Next_Tmax</th>\n",
       "      <td>7725.0</td>\n",
       "      <td>30.274887</td>\n",
       "      <td>3.128010</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.600000</td>\n",
       "      <td>38.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Next_Tmin</th>\n",
       "      <td>7725.0</td>\n",
       "      <td>22.932220</td>\n",
       "      <td>2.487613</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>21.300000</td>\n",
       "      <td>23.100000</td>\n",
       "      <td>24.600000</td>\n",
       "      <td>29.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count         mean         std          min          25%  \\\n",
       "station           7750.0    13.000000    7.211568     1.000000     7.000000   \n",
       "Present_Tmax      7682.0    29.768211    2.969999    20.000000    27.800000   \n",
       "Present_Tmin      7682.0    23.225059    2.413961    11.300000    21.700000   \n",
       "LDAPS_RHmin       7677.0    56.759372   14.668111    19.794666    45.963543   \n",
       "LDAPS_RHmax       7677.0    88.374804    7.192004    58.936283    84.222862   \n",
       "LDAPS_Tmax_lapse  7677.0    29.613447    2.947191    17.624954    27.673499   \n",
       "LDAPS_Tmin_lapse  7677.0    23.512589    2.345347    14.272646    22.089739   \n",
       "LDAPS_WS          7677.0     7.097875    2.183836     2.882580     5.678705   \n",
       "LDAPS_LH          7677.0    62.505019   33.730589   -13.603212    37.266753   \n",
       "LDAPS_CC1         7677.0     0.368774    0.262458     0.000000     0.146654   \n",
       "LDAPS_CC2         7677.0     0.356080    0.258061     0.000000     0.140615   \n",
       "LDAPS_CC3         7677.0     0.318404    0.250362     0.000000     0.101388   \n",
       "LDAPS_CC4         7677.0     0.299191    0.254348     0.000000     0.081532   \n",
       "LDAPS_PPT1        7677.0     0.591995    1.945768     0.000000     0.000000   \n",
       "LDAPS_PPT2        7677.0     0.485003    1.762807     0.000000     0.000000   \n",
       "LDAPS_PPT3        7677.0     0.278200    1.161809     0.000000     0.000000   \n",
       "LDAPS_PPT4        7677.0     0.269407    1.206214     0.000000     0.000000   \n",
       "lat               7752.0    37.544722    0.050352    37.456200    37.510200   \n",
       "lon               7752.0   126.991397    0.079435   126.826000   126.937000   \n",
       "DEM               7752.0    61.867972   54.279780    12.370000    28.700000   \n",
       "Slope             7752.0     1.257048    1.370444     0.098475     0.271300   \n",
       "Solar radiation   7752.0  5341.502803  429.158867  4329.520508  4999.018555   \n",
       "Next_Tmax         7725.0    30.274887    3.128010    17.400000    28.200000   \n",
       "Next_Tmin         7725.0    22.932220    2.487613    11.300000    21.300000   \n",
       "\n",
       "                          50%          75%          max  \n",
       "station             13.000000    19.000000    25.000000  \n",
       "Present_Tmax        29.900000    32.000000    37.600000  \n",
       "Present_Tmin        23.400000    24.900000    29.900000  \n",
       "LDAPS_RHmin         55.039024    67.190056    98.524734  \n",
       "LDAPS_RHmax         89.793480    93.743629   100.000153  \n",
       "LDAPS_Tmax_lapse    29.703426    31.710450    38.542255  \n",
       "LDAPS_Tmin_lapse    23.760199    25.152909    29.619342  \n",
       "LDAPS_WS             6.547470     8.032276    21.857621  \n",
       "LDAPS_LH            56.865482    84.223616   213.414006  \n",
       "LDAPS_CC1            0.315697     0.575489     0.967277  \n",
       "LDAPS_CC2            0.312421     0.558694     0.968353  \n",
       "LDAPS_CC3            0.262555     0.496703     0.983789  \n",
       "LDAPS_CC4            0.227664     0.499489     0.974710  \n",
       "LDAPS_PPT1           0.000000     0.052525    23.701544  \n",
       "LDAPS_PPT2           0.000000     0.018364    21.621661  \n",
       "LDAPS_PPT3           0.000000     0.007896    15.841235  \n",
       "LDAPS_PPT4           0.000000     0.000041    16.655469  \n",
       "lat                 37.550700    37.577600    37.645000  \n",
       "lon                126.995000   127.042000   127.135000  \n",
       "DEM                 45.716000    59.832400   212.335000  \n",
       "Slope                0.618000     1.767800     5.178230  \n",
       "Solar radiation   5436.345215  5728.316406  5992.895996  \n",
       "Next_Tmax           30.500000    32.600000    38.900000  \n",
       "Next_Tmin           23.100000    24.600000    29.800000  "
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.describe().T "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:45.952260Z",
     "start_time": "2020-06-16T00:12:45.895274Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7752 entries, 0 to 7751\n",
      "Data columns (total 25 columns):\n",
      "station             7750 non-null float64\n",
      "Date                7750 non-null object\n",
      "Present_Tmax        7682 non-null float64\n",
      "Present_Tmin        7682 non-null float64\n",
      "LDAPS_RHmin         7677 non-null float64\n",
      "LDAPS_RHmax         7677 non-null float64\n",
      "LDAPS_Tmax_lapse    7677 non-null float64\n",
      "LDAPS_Tmin_lapse    7677 non-null float64\n",
      "LDAPS_WS            7677 non-null float64\n",
      "LDAPS_LH            7677 non-null float64\n",
      "LDAPS_CC1           7677 non-null float64\n",
      "LDAPS_CC2           7677 non-null float64\n",
      "LDAPS_CC3           7677 non-null float64\n",
      "LDAPS_CC4           7677 non-null float64\n",
      "LDAPS_PPT1          7677 non-null float64\n",
      "LDAPS_PPT2          7677 non-null float64\n",
      "LDAPS_PPT3          7677 non-null float64\n",
      "LDAPS_PPT4          7677 non-null float64\n",
      "lat                 7752 non-null float64\n",
      "lon                 7752 non-null float64\n",
      "DEM                 7752 non-null float64\n",
      "Slope               7752 non-null float64\n",
      "Solar radiation     7752 non-null float64\n",
      "Next_Tmax           7725 non-null float64\n",
      "Next_Tmin           7725 non-null float64\n",
      "dtypes: float64(24), object(1)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "Data.info() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看空值（5分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:46.101859Z",
     "start_time": "2020-06-16T00:12:45.955239Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "station              2\n",
       "Date                 2\n",
       "Present_Tmax        70\n",
       "Present_Tmin        70\n",
       "LDAPS_RHmin         75\n",
       "LDAPS_RHmax         75\n",
       "LDAPS_Tmax_lapse    75\n",
       "LDAPS_Tmin_lapse    75\n",
       "LDAPS_WS            75\n",
       "LDAPS_LH            75\n",
       "LDAPS_CC1           75\n",
       "LDAPS_CC2           75\n",
       "LDAPS_CC3           75\n",
       "LDAPS_CC4           75\n",
       "LDAPS_PPT1          75\n",
       "LDAPS_PPT2          75\n",
       "LDAPS_PPT3          75\n",
       "LDAPS_PPT4          75\n",
       "lat                  0\n",
       "lon                  0\n",
       "DEM                  0\n",
       "Slope                0\n",
       "Solar radiation      0\n",
       "Next_Tmax           27\n",
       "Next_Tmin           27\n",
       "dtype: int64"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.isna().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(164, 25)"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data[Data.isna().T.any()].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:46.301511Z",
     "start_time": "2020-06-16T00:12:46.110848Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27, 25)"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取出所有有空值的记录  （5分）\n",
    "Data[Data['Next_Tmax'].isna().T].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看每列数据的分布状态"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:55.448321Z",
     "start_time": "2020-06-16T00:12:46.308487Z"
    }
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot convert float NaN to integer",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-338-9b5d2b45a790>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mI\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mI\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36mdistplot\u001b[1;34m(a, bins, hist, kde, rug, fit, hist_kws, kde_kws, rug_kws, fit_kws, color, vertical, norm_hist, axlabel, label, ax)\u001b[0m\n\u001b[0;32m    213\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mhist\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    214\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mbins\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 215\u001b[1;33m             \u001b[0mbins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_freedman_diaconis_bins\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m50\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    216\u001b[0m         \u001b[0mhist_kws\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"alpha\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    217\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mLooseVersion\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmpl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mLooseVersion\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"2.2\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36m_freedman_diaconis_bins\u001b[1;34m(a)\u001b[0m\n\u001b[0;32m     37\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     38\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 39\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mceil\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     40\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: cannot convert float NaN to integer"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制每列数据的直方图（分布频次）\n",
    "plot = Data.drop('Date',axis=1) # 删除'Date'列\n",
    "\n",
    "for I in plot.columns:\n",
    "    sns.distplot(plot[I]) \n",
    "    plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 生成数据概况报告"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:21.940528Z",
     "start_time": "2020-06-16T00:12:55.451319Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pp' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-339-183bb177293f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# 生成整体数据概要\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdata_report\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mProfileReport\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mData\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mdata_report\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m# 也可以把生成的报告导出保存\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'pp' is not defined"
     ]
    }
   ],
   "source": [
    "# 生成整体数据概要\n",
    "data_report=pp.ProfileReport(Data) \n",
    "data_report\n",
    "\n",
    "# 也可以把生成的报告导出保存\n",
    "data_report.to_file('report.html') \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 提取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:33:52.629282Z",
     "start_time": "2020-06-16T01:33:52.621285Z"
    }
   },
   "outputs": [],
   "source": [
    "# 由于本数据集的特殊性（有两个样本标签：次日最高气温和最低气温）\n",
    "#，本案例中我们只进行次日最高温预测--因此这里只选出与最高温预测相关的变量，作为建模要用的数据\n",
    "\n",
    "Max = Data[['Present_Tmax','LDAPS_RHmax','LDAPS_Tmax_lapse','LDAPS_WS','LDAPS_LH','LDAPS_CC1','LDAPS_CC2','LDAPS_CC3','LDAPS_CC4','LDAPS_PPT1','LDAPS_PPT4',\n",
    "          'lat','lon','DEM','Slope','Solar radiation','Next_Tmax']] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:33:56.575804Z",
     "start_time": "2020-06-16T01:33:56.551818Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28.7</td>\n",
       "      <td>91.116364</td>\n",
       "      <td>28.074101</td>\n",
       "      <td>6.818887</td>\n",
       "      <td>69.451805</td>\n",
       "      <td>0.233947</td>\n",
       "      <td>0.203896</td>\n",
       "      <td>0.161697</td>\n",
       "      <td>0.130928</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.7850</td>\n",
       "      <td>5992.895996</td>\n",
       "      <td>29.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31.9</td>\n",
       "      <td>90.604721</td>\n",
       "      <td>29.850689</td>\n",
       "      <td>5.691890</td>\n",
       "      <td>51.937448</td>\n",
       "      <td>0.225508</td>\n",
       "      <td>0.251771</td>\n",
       "      <td>0.159444</td>\n",
       "      <td>0.127727</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>127.032</td>\n",
       "      <td>44.7624</td>\n",
       "      <td>0.5141</td>\n",
       "      <td>5869.312500</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.6</td>\n",
       "      <td>83.973587</td>\n",
       "      <td>30.091292</td>\n",
       "      <td>6.138224</td>\n",
       "      <td>20.573050</td>\n",
       "      <td>0.209344</td>\n",
       "      <td>0.257469</td>\n",
       "      <td>0.204091</td>\n",
       "      <td>0.142125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5776</td>\n",
       "      <td>127.058</td>\n",
       "      <td>33.3068</td>\n",
       "      <td>0.2661</td>\n",
       "      <td>5863.555664</td>\n",
       "      <td>31.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.0</td>\n",
       "      <td>96.483688</td>\n",
       "      <td>29.704629</td>\n",
       "      <td>5.650050</td>\n",
       "      <td>65.727144</td>\n",
       "      <td>0.216372</td>\n",
       "      <td>0.226002</td>\n",
       "      <td>0.161157</td>\n",
       "      <td>0.134249</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6450</td>\n",
       "      <td>127.022</td>\n",
       "      <td>45.7160</td>\n",
       "      <td>2.5348</td>\n",
       "      <td>5856.964844</td>\n",
       "      <td>31.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31.4</td>\n",
       "      <td>90.155128</td>\n",
       "      <td>29.113934</td>\n",
       "      <td>5.735004</td>\n",
       "      <td>107.965535</td>\n",
       "      <td>0.151407</td>\n",
       "      <td>0.249995</td>\n",
       "      <td>0.178892</td>\n",
       "      <td>0.170021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>127.135</td>\n",
       "      <td>35.0380</td>\n",
       "      <td>0.5055</td>\n",
       "      <td>5859.552246</td>\n",
       "      <td>31.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS    LDAPS_LH  \\\n",
       "0          28.7    91.116364         28.074101  6.818887   69.451805   \n",
       "1          31.9    90.604721         29.850689  5.691890   51.937448   \n",
       "2          31.6    83.973587         30.091292  6.138224   20.573050   \n",
       "3          32.0    96.483688         29.704629  5.650050   65.727144   \n",
       "4          31.4    90.155128         29.113934  5.735004  107.965535   \n",
       "\n",
       "   LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0   0.233947   0.203896   0.161697   0.130928         0.0         0.0   \n",
       "1   0.225508   0.251771   0.159444   0.127727         0.0         0.0   \n",
       "2   0.209344   0.257469   0.204091   0.142125         0.0         0.0   \n",
       "3   0.216372   0.226002   0.161157   0.134249         0.0         0.0   \n",
       "4   0.151407   0.249995   0.178892   0.170021         0.0         0.0   \n",
       "\n",
       "       lat      lon       DEM   Slope  Solar radiation  Next_Tmax  \n",
       "0  37.6046  126.991  212.3350  2.7850      5992.895996       29.1  \n",
       "1  37.6046  127.032   44.7624  0.5141      5869.312500       30.5  \n",
       "2  37.5776  127.058   33.3068  0.2661      5863.555664       31.1  \n",
       "3  37.6450  127.022   45.7160  2.5348      5856.964844       31.7  \n",
       "4  37.5507  127.135   35.0380  0.5055      5859.552246       31.2  "
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:22.290321Z",
     "start_time": "2020-06-16T00:15:22.130413Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7752 entries, 0 to 7751\n",
      "Data columns (total 17 columns):\n",
      "Present_Tmax        7682 non-null float64\n",
      "LDAPS_RHmax         7677 non-null float64\n",
      "LDAPS_Tmax_lapse    7677 non-null float64\n",
      "LDAPS_WS            7677 non-null float64\n",
      "LDAPS_LH            7677 non-null float64\n",
      "LDAPS_CC1           7677 non-null float64\n",
      "LDAPS_CC2           7677 non-null float64\n",
      "LDAPS_CC3           7677 non-null float64\n",
      "LDAPS_CC4           7677 non-null float64\n",
      "LDAPS_PPT1          7677 non-null float64\n",
      "LDAPS_PPT4          7677 non-null float64\n",
      "lat                 7752 non-null float64\n",
      "lon                 7752 non-null float64\n",
      "DEM                 7752 non-null float64\n",
      "Slope               7752 non-null float64\n",
      "Solar radiation     7752 non-null float64\n",
      "Next_Tmax           7725 non-null float64\n",
      "dtypes: float64(17)\n",
      "memory usage: 1.0 MB\n"
     ]
    }
   ],
   "source": [
    "Max.info() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:22.570160Z",
     "start_time": "2020-06-16T00:15:22.293320Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Present_Tmax</th>\n",
       "      <td>7682.0</td>\n",
       "      <td>29.768211</td>\n",
       "      <td>2.969999</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>27.800000</td>\n",
       "      <td>29.900000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>37.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>88.374804</td>\n",
       "      <td>7.192004</td>\n",
       "      <td>58.936283</td>\n",
       "      <td>84.222862</td>\n",
       "      <td>89.793480</td>\n",
       "      <td>93.743629</td>\n",
       "      <td>100.000153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>29.613447</td>\n",
       "      <td>2.947191</td>\n",
       "      <td>17.624954</td>\n",
       "      <td>27.673499</td>\n",
       "      <td>29.703426</td>\n",
       "      <td>31.710450</td>\n",
       "      <td>38.542255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>7.097875</td>\n",
       "      <td>2.183836</td>\n",
       "      <td>2.882580</td>\n",
       "      <td>5.678705</td>\n",
       "      <td>6.547470</td>\n",
       "      <td>8.032276</td>\n",
       "      <td>21.857621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>62.505019</td>\n",
       "      <td>33.730589</td>\n",
       "      <td>-13.603212</td>\n",
       "      <td>37.266753</td>\n",
       "      <td>56.865482</td>\n",
       "      <td>84.223616</td>\n",
       "      <td>213.414006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.368774</td>\n",
       "      <td>0.262458</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.146654</td>\n",
       "      <td>0.315697</td>\n",
       "      <td>0.575489</td>\n",
       "      <td>0.967277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.356080</td>\n",
       "      <td>0.258061</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.140615</td>\n",
       "      <td>0.312421</td>\n",
       "      <td>0.558694</td>\n",
       "      <td>0.968353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.318404</td>\n",
       "      <td>0.250362</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.101388</td>\n",
       "      <td>0.262555</td>\n",
       "      <td>0.496703</td>\n",
       "      <td>0.983789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.299191</td>\n",
       "      <td>0.254348</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.081532</td>\n",
       "      <td>0.227664</td>\n",
       "      <td>0.499489</td>\n",
       "      <td>0.974710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.591995</td>\n",
       "      <td>1.945768</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052525</td>\n",
       "      <td>23.701544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.269407</td>\n",
       "      <td>1.206214</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>16.655469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>37.544722</td>\n",
       "      <td>0.050352</td>\n",
       "      <td>37.456200</td>\n",
       "      <td>37.510200</td>\n",
       "      <td>37.550700</td>\n",
       "      <td>37.577600</td>\n",
       "      <td>37.645000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lon</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>126.991397</td>\n",
       "      <td>0.079435</td>\n",
       "      <td>126.826000</td>\n",
       "      <td>126.937000</td>\n",
       "      <td>126.995000</td>\n",
       "      <td>127.042000</td>\n",
       "      <td>127.135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEM</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>61.867972</td>\n",
       "      <td>54.279780</td>\n",
       "      <td>12.370000</td>\n",
       "      <td>28.700000</td>\n",
       "      <td>45.716000</td>\n",
       "      <td>59.832400</td>\n",
       "      <td>212.335000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slope</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>1.257048</td>\n",
       "      <td>1.370444</td>\n",
       "      <td>0.098475</td>\n",
       "      <td>0.271300</td>\n",
       "      <td>0.618000</td>\n",
       "      <td>1.767800</td>\n",
       "      <td>5.178230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solar radiation</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>5341.502803</td>\n",
       "      <td>429.158867</td>\n",
       "      <td>4329.520508</td>\n",
       "      <td>4999.018555</td>\n",
       "      <td>5436.345215</td>\n",
       "      <td>5728.316406</td>\n",
       "      <td>5992.895996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Next_Tmax</th>\n",
       "      <td>7725.0</td>\n",
       "      <td>30.274887</td>\n",
       "      <td>3.128010</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.600000</td>\n",
       "      <td>38.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count         mean         std          min          25%  \\\n",
       "Present_Tmax      7682.0    29.768211    2.969999    20.000000    27.800000   \n",
       "LDAPS_RHmax       7677.0    88.374804    7.192004    58.936283    84.222862   \n",
       "LDAPS_Tmax_lapse  7677.0    29.613447    2.947191    17.624954    27.673499   \n",
       "LDAPS_WS          7677.0     7.097875    2.183836     2.882580     5.678705   \n",
       "LDAPS_LH          7677.0    62.505019   33.730589   -13.603212    37.266753   \n",
       "LDAPS_CC1         7677.0     0.368774    0.262458     0.000000     0.146654   \n",
       "LDAPS_CC2         7677.0     0.356080    0.258061     0.000000     0.140615   \n",
       "LDAPS_CC3         7677.0     0.318404    0.250362     0.000000     0.101388   \n",
       "LDAPS_CC4         7677.0     0.299191    0.254348     0.000000     0.081532   \n",
       "LDAPS_PPT1        7677.0     0.591995    1.945768     0.000000     0.000000   \n",
       "LDAPS_PPT4        7677.0     0.269407    1.206214     0.000000     0.000000   \n",
       "lat               7752.0    37.544722    0.050352    37.456200    37.510200   \n",
       "lon               7752.0   126.991397    0.079435   126.826000   126.937000   \n",
       "DEM               7752.0    61.867972   54.279780    12.370000    28.700000   \n",
       "Slope             7752.0     1.257048    1.370444     0.098475     0.271300   \n",
       "Solar radiation   7752.0  5341.502803  429.158867  4329.520508  4999.018555   \n",
       "Next_Tmax         7725.0    30.274887    3.128010    17.400000    28.200000   \n",
       "\n",
       "                          50%          75%          max  \n",
       "Present_Tmax        29.900000    32.000000    37.600000  \n",
       "LDAPS_RHmax         89.793480    93.743629   100.000153  \n",
       "LDAPS_Tmax_lapse    29.703426    31.710450    38.542255  \n",
       "LDAPS_WS             6.547470     8.032276    21.857621  \n",
       "LDAPS_LH            56.865482    84.223616   213.414006  \n",
       "LDAPS_CC1            0.315697     0.575489     0.967277  \n",
       "LDAPS_CC2            0.312421     0.558694     0.968353  \n",
       "LDAPS_CC3            0.262555     0.496703     0.983789  \n",
       "LDAPS_CC4            0.227664     0.499489     0.974710  \n",
       "LDAPS_PPT1           0.000000     0.052525    23.701544  \n",
       "LDAPS_PPT4           0.000000     0.000041    16.655469  \n",
       "lat                 37.550700    37.577600    37.645000  \n",
       "lon                126.995000   127.042000   127.135000  \n",
       "DEM                 45.716000    59.832400   212.335000  \n",
       "Slope                0.618000     1.767800     5.178230  \n",
       "Solar radiation   5436.345215  5728.316406  5992.895996  \n",
       "Next_Tmax           30.500000    32.600000    38.900000  "
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 填充缺失值（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:34:07.620687Z",
     "start_time": "2020-06-16T01:34:07.603697Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\Anaconda3\\envs\\py36\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "# 用每列的均值填充其缺失值\n",
    "col=Max.columns\n",
    "for i in col:\n",
    "    Max[i]=Max1[i].fillna(Max[i].mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:34:10.245846Z",
     "start_time": "2020-06-16T01:34:10.235840Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Present_Tmax        0\n",
       "LDAPS_RHmax         0\n",
       "LDAPS_Tmax_lapse    0\n",
       "LDAPS_WS            0\n",
       "LDAPS_LH            0\n",
       "LDAPS_CC1           0\n",
       "LDAPS_CC2           0\n",
       "LDAPS_CC3           0\n",
       "LDAPS_CC4           0\n",
       "LDAPS_PPT1          0\n",
       "LDAPS_PPT4          0\n",
       "lat                 0\n",
       "lon                 0\n",
       "DEM                 0\n",
       "Slope               0\n",
       "Solar radiation     0\n",
       "Next_Tmax           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max.isnull().sum() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 标准化处理连续型特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:15:24.140777Z",
     "start_time": "2020-06-16T02:15:24.099800Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\Anaconda3\\envs\\py36\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\ASUS\\Anaconda3\\envs\\py36\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\ASUS\\Anaconda3\\envs\\py36\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\ASUS\\Anaconda3\\envs\\py36\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\ASUS\\Anaconda3\\envs\\py36\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\ASUS\\Anaconda3\\envs\\py36\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\ASUS\\Anaconda3\\envs\\py36\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\ASUS\\Anaconda3\\envs\\py36\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\ASUS\\Anaconda3\\envs\\py36\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\ASUS\\Anaconda3\\envs\\py36\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\ASUS\\Anaconda3\\envs\\py36\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\ASUS\\Anaconda3\\envs\\py36\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\ASUS\\Anaconda3\\envs\\py36\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\ASUS\\Anaconda3\\envs\\py36\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\ASUS\\Anaconda3\\envs\\py36\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\ASUS\\Anaconda3\\envs\\py36\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\ASUS\\Anaconda3\\envs\\py36\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "      <td>-0.376282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "      <td>0.072097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "      <td>0.264260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "      <td>0.456422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "      <td>0.296287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  LDAPS_CC1  \\\n",
       "0     -0.361326     0.383078         -0.524889 -0.128382  0.206966  -0.516243   \n",
       "1      0.721084     0.311586          0.080895 -0.646994 -0.314841  -0.548557   \n",
       "2      0.619608    -0.614982          0.162936 -0.441604 -1.249283  -0.610450   \n",
       "3      0.754909     1.133054          0.031092 -0.666247  0.095997  -0.583539   \n",
       "4      0.551957     0.248765         -0.170325 -0.627154  1.354409  -0.832287   \n",
       "\n",
       "   LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4       lat  \\\n",
       "0  -0.592636  -0.629013  -0.664815    -0.30575   -0.224453  1.189286   \n",
       "1  -0.406199  -0.638055  -0.677462    -0.30575   -0.224453  1.189286   \n",
       "2  -0.384009  -0.458843  -0.620575    -0.30575   -0.224453  0.653021   \n",
       "3  -0.506548  -0.631178  -0.651696    -0.30575   -0.224453  1.991696   \n",
       "4  -0.413115  -0.559990  -0.510358    -0.30575   -0.224453  0.118743   \n",
       "\n",
       "        lon       DEM     Slope  Solar radiation  Next_Tmax  \n",
       "0 -0.005000  2.772243  1.115004         1.517935  -0.376282  \n",
       "1  0.511177 -0.315157 -0.542158         1.229950   0.072097  \n",
       "2  0.838510 -0.526218 -0.723133         1.216534   0.264260  \n",
       "3  0.385280 -0.297588  0.932424         1.201176   0.456422  \n",
       "4  1.807917 -0.494322 -0.548433         1.207205   0.296287  "
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler \n",
    "#标准化处理连续型特征 （10分）\n",
    "col=[i for i in Max.columns]\n",
    "for i in col:\n",
    "     Max[i]=StandardScaler().fit_transform(pd.DataFrame(Max[i])) \n",
    "Max.head()\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "      <td>-0.376282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "      <td>0.072097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "      <td>0.264260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "      <td>0.456422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "      <td>0.296287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  LDAPS_CC1  \\\n",
       "0     -0.361326     0.383078         -0.524889 -0.128382  0.206966  -0.516243   \n",
       "1      0.721084     0.311586          0.080895 -0.646994 -0.314841  -0.548557   \n",
       "2      0.619608    -0.614982          0.162936 -0.441604 -1.249283  -0.610450   \n",
       "3      0.754909     1.133054          0.031092 -0.666247  0.095997  -0.583539   \n",
       "4      0.551957     0.248765         -0.170325 -0.627154  1.354409  -0.832287   \n",
       "\n",
       "   LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4       lat  \\\n",
       "0  -0.592636  -0.629013  -0.664815    -0.30575   -0.224453  1.189286   \n",
       "1  -0.406199  -0.638055  -0.677462    -0.30575   -0.224453  1.189286   \n",
       "2  -0.384009  -0.458843  -0.620575    -0.30575   -0.224453  0.653021   \n",
       "3  -0.506548  -0.631178  -0.651696    -0.30575   -0.224453  1.991696   \n",
       "4  -0.413115  -0.559990  -0.510358    -0.30575   -0.224453  0.118743   \n",
       "\n",
       "        lon       DEM     Slope  Solar radiation  Next_Tmax  \n",
       "0 -0.005000  2.772243  1.115004         1.517935  -0.376282  \n",
       "1  0.511177 -0.315157 -0.542158         1.229950   0.072097  \n",
       "2  0.838510 -0.526218 -0.723133         1.216534   0.264260  \n",
       "3  0.385280 -0.297588  0.932424         1.201176   0.456422  \n",
       "4  1.807917 -0.494322 -0.548433         1.207205   0.296287  "
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Scaled_Data=Max\n",
    "Scaled_Data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 划分特征列和标签列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:34:50.260685Z",
     "start_time": "2020-06-16T01:34:50.234704Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  LDAPS_CC1  \\\n",
       "0     -0.361326     0.383078         -0.524889 -0.128382  0.206966  -0.516243   \n",
       "1      0.721084     0.311586          0.080895 -0.646994 -0.314841  -0.548557   \n",
       "2      0.619608    -0.614982          0.162936 -0.441604 -1.249283  -0.610450   \n",
       "3      0.754909     1.133054          0.031092 -0.666247  0.095997  -0.583539   \n",
       "4      0.551957     0.248765         -0.170325 -0.627154  1.354409  -0.832287   \n",
       "\n",
       "   LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4       lat  \\\n",
       "0  -0.592636  -0.629013  -0.664815    -0.30575   -0.224453  1.189286   \n",
       "1  -0.406199  -0.638055  -0.677462    -0.30575   -0.224453  1.189286   \n",
       "2  -0.384009  -0.458843  -0.620575    -0.30575   -0.224453  0.653021   \n",
       "3  -0.506548  -0.631178  -0.651696    -0.30575   -0.224453  1.991696   \n",
       "4  -0.413115  -0.559990  -0.510358    -0.30575   -0.224453  0.118743   \n",
       "\n",
       "        lon       DEM     Slope  Solar radiation  \n",
       "0 -0.005000  2.772243  1.115004         1.517935  \n",
       "1  0.511177 -0.315157 -0.542158         1.229950  \n",
       "2  0.838510 -0.526218 -0.723133         1.216534  \n",
       "3  0.385280 -0.297588  0.932424         1.201176  \n",
       "4  1.807917 -0.494322 -0.548433         1.207205  "
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(7752, 16)"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = Scaled_Data.drop(['Next_Tmax'],axis=1) \n",
    "X.head() \n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:09.014361Z",
     "start_time": "2020-06-16T01:35:09.005367Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      -0.376282\n",
       "1       0.072097\n",
       "2       0.264260\n",
       "3       0.456422\n",
       "4       0.296287\n",
       "          ...   \n",
       "7747   -0.632499\n",
       "7748   -0.536418\n",
       "7749   -0.792634\n",
       "7750   -4.123453\n",
       "7751    2.762374\n",
       "Name: Next_Tmax, Length: 7752, dtype: float64"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = Scaled_Data['Next_Tmax']\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 创建线性回归模型并评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 划分数据集（5分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:15.723572Z",
     "start_time": "2020-06-16T01:35:15.714578Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split,cross_val_score\n",
    "from sklearn.linear_model import LinearRegression as lr\n",
    "from sklearn.metrics import mean_squared_error \n",
    "x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 建模（5分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:18.264310Z",
     "start_time": "2020-06-16T01:35:18.247275Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=lr()\n",
    "model.fit(x_train,y_train)\n",
    "y_pred=model.predict(x_test)\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T06:32:40.607647Z",
     "start_time": "2020-06-15T06:32:40.603648Z"
    }
   },
   "source": [
    "## 评估（5分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:23.925342Z",
     "start_time": "2020-06-16T01:35:23.911350Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7587262924411362"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.736076944968986"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_train,y_train)\n",
    "model.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 计算MSE与RMSE（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:22:24.025817Z",
     "start_time": "2020-06-16T01:22:24.016823Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2554011051627178 0.505372244155452\n"
     ]
    }
   ],
   "source": [
    "MSE=mean_squared_error(y_test,y_pred) \n",
    "RMSE=np.sqrt(MSE) \n",
    "print(MSE,RMSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型诊断"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:24.480812Z",
     "start_time": "2020-06-16T00:15:24.344885Z"
    }
   },
   "source": [
    "'''\n",
    "从上可以看到RMSE(越小越好）的值0.5，我们通过可视化来看一下训练后的预测和真实值之间的差异：\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:22:30.193284Z",
     "start_time": "2020-06-16T01:22:29.785111Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x8e486208>]"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x8ce07908>]"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x8e486588>"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画个折线图来看看模型拟合的好坏程度\n",
    "plt.figure(figsize=(20,10)) \n",
    "plt.plot(range(len(y_test)), y_test, 'r', label='Y-TEST')\n",
    "plt.plot(range(len(y_test)), y_pred, 'b', label='Y-PREDICTED')\n",
    "plt.legend() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:25.123437Z",
     "start_time": "2020-06-16T00:15:25.118441Z"
    }
   },
   "source": [
    "'''\n",
    "可以看到许多地方，红线会明显超出蓝线，说明我们的模型拟合度不够，再换个散点图来更直观地看一下：\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8e83ec18>"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'y_test')"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y_pred')"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEJCAYAAACZjSCSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOy9eZBkV33n+znn3ps319q6q6vV6pYaLUgaoMcCAQJhEHiEzLBNNBgklsFjiACNB3hM8PwYmRfMPAYZRzCBBQ7LjgEjO8YjiaU9Mk8WlpDQyEi2UQPPEqKF1N1qqaXurq69cr15l/P+OHlvZVZlVmXtWV3nE6GlsjJvnszK/P3O+S3fn1BKKQwGg8GwbZGbvQCDwWAwbC7GERgMBsM2xzgCg8Fg2OYYR2AwGAzbHOMIDAaDYZtjb/YCuqVWq/GLX/yC4eFhLMva7OUYDAbDliAMQ8bGxnj5y19OOp1ue58t4wh+8Ytf8MEPfnCzl2EwGAxbkr/6q7/iqquuavu7LeMIhoeHAf1idu/evcmrMRgMhq3BmTNn+OAHP5jY0HZsGUcQh4N2797N3r17N3k1BoPBsLVYLKRuksUGg8GwzTGOwGAwGLY5xhEYDAbDNsc4AoPBYNjmGEdgMBgM2xzjCAwGg2Gbs2XKRw0Gg2G9OHxklEMPHWV0ssLIUJaD117CVVeMbPayNgxzIjAYDNuaw0dG+bNDjzM1W6WQsZmarfJnhx7n8JHRzV7aqokixeNHx5a8nzkRGAyGbc2hh45i24J0SpvDdMqmRsChh45u2VNBGEY8ePgk3/vRUU489/yS9zeOwGAwbGtGJysUMq2m0HUszk5WNmlFK8cPIv7uH0/w1w8d4+xU9+s3jsBgMGxrRoayTM1WkxMBgOeH7BrKbuKqlketHnDPj49z98PHmSp6AAjgyst28aqL9/F/Prj4440jMBgM25qD117Cnx16nBoBrmPh+SFBoDh47SWbvbQlKVd9/tfDR/l/f/wspYoPgBSC17xsN7/5ugs5b2eOqfGlcx3GERgMhm3NVVeMwMEDHHroKGcnK+zaAlVDM6Ua3/vRUX7wD89R9QIAbEvy+gPn8dbXXsiuwQxZ1yGbcYhq7pLXM47AYDBse666YqSnDX/M+HSF7zzwDD987HnqfgTofMavX3k+173mAoYKLtm0Qy7jYFndF4UaR2AwGLYFW7lX4NRYiW8/8DT/+2cvEIQKgKxr8+ar9vHmq/YxkEuRTdtkMyksKZZ9/U1xBH/8x3/MvffeC8Cb3vQmfu/3fm8zlmEwGLYJca+AbYuWXgEOHuhpZ/Dc6RnuvP9pHnn8FErbfwpZh3/1mgt445V76cumyGVsMq6DXIEDiNlwR/Doo4/y4x//mL/+679GCMHHPvYx7r//fq677rqNXorBYNgmbLVegaefn+LO+3/FY7+cS/QO9rm89TUXcs2/3EM+EzsAGyFW7gBiNtwRDA8P87nPfY5UKgXAxRdfzKlTpzZ6GQaDYRvRqVfg+TOz3HzbIz0TLnri6Dh33v8rHj86nty2azDD9Vfv5+qX727E//UJYC3ZcEdw6aWXJv9/4sQJ7r33Xu64446NXobBYNhixDH+k2eK+GGEbQku2N3XlfFu1yswXfIoV32efm6KIIyYKXp87a6f86n3X7nk9dYy36CU4qdHznLnD3/Fr56bSm4/fzjP216/n1ddvqtRAWS3rH8t2bRk8TPPPMPHP/5xfu/3fo/9+/dv1jIMBsMWII7x+2FEsVIHoRumTo0Vu4r1t+sVmCnViSKFEAohBPUgxCuG/D/f/Ef6cy77dhfaGvi1yjdEkeKRx0/x7R8+zYnTs8ntL9nTx9tet58Dl+4k6zo8e2qGu//+OC+cLa3bqWVTHMFPf/pTPvWpT3HzzTfz9re/fTOWYDAYthBxjH+mHCClQApBFCkqtYAd/faSsf52vQIvjpVBaVmGZpTSp4WZox5PHB1n/3l9fOTt/yK5/mrzDWEY8aOfvcB3H3har6HB5RcO8puv288V+wfJpVNkMw6PHx3jT7+3/knuDXcEp0+f5nd/93f56le/yute97qNfnqDwbAFiWP8fhARl8cLoY14sy7QYiGb5l6Bw0dGeaIpDt+ORpEOz5+Z5Zbbf0I2bXPB7j6ePzPLzv50y3270Saq+yE//MlzfPdHRxmbqia3H7hkJ7/5uv1cuq+fXNohm57rAfjejzYmyb3hjuCb3/wmnufx5S9/Obnthhtu4MYbb9zopRgMhh5hMQN++Mgo5arPxHQVpbSBtqVAKXBsmegCLSdkc+iho9iWSGryFyNSIJTCq4dMzVap1AKmLY/BwpwzWEybqOYF/O2jz/K//vexFh2gV12xi9983X727+4jl3XIuM6CHoCNEsTbcEfw+c9/ns9//vMb/bQGg6FHmG/0X3HxDu599ASVWkDdDxmbqvDksXEu2N3H6w+cx4OPnSSdknh1UCjCEJQKkUKQTTuJLlC7kM2U5/GHf/kYYaQN/p7hHNcc2MNTJyaJoqWdQIwlBH4QMTHjEYYRk7MeIBjIpzpqE5WqPn/z8DG+/+PjczpAUnD1y3Zz/ev2s3c4Ty7rkF2kBHSjBPFMZ7HBYNgw2u3av/3Dp1t35o1d/wtni3zngRJ9OYfBQpqUbTFd8vDqISDIZRz2DM8ldG879HjL7rlcC5iYqSU/C+C500VOnvkVUkosKRBAuMSpQAChUkSRImhUKwWholiuE4ZqQVJ5qljjrx86xr2PPkutHgL65HLNgT289bUXcN6OPLlsdyWgGyWIZxyBwWBYd+JTwFMnJpFCMNSfRgi9e+8UnglChSWhXA0YLEAuozV0lFKUqgHf+P3WJtT5u+fx6WrL7+NniRRYQjuAuFtXNH4vmu6X0LivlDSS1OCmJDv60wz2ZbjlpmsAGJuq8t0Hn+H+nzyXJKDdlMWbrtzLda/dx66BHLm0jet2b3Y3ShDPOAKDwbCuNJ8ClFL4UcSZ8TJSClKOtehjHVsuqOrpFBqZv3tuflwceYkNfxhGCCGIGjcoYLDg8rbX7+eJYxNJr4JqnAL8IMKWulJJoRjIp5NY/anxEt/+4dM89NMXkhBULm3zloYO0M7+DLmMs+Rr7cRGCOIZR2AwGLrmjvue4u6Hj1P1AjKuzbvfeBE3vvXyRR/THLuXYi5BGyltZBcjl3EoluvU6gtDI4ePjHL7PU9yaqxMpBS2JbEECClxLIkQ2vAn0femrX6kwJI68Ro/NpexeeLYRLLDb37Nd97/NPVAIYWiP5cil3Goej5hFPGJLz+QOJi+XIp/9ZoLuPaV5zPUlyGbdnDs3h8NbxyBwWDoijvue4q77n8aBNhST8W66/6nARZ1Bs2VL6rJGqs2NfzzcSzJe3/j0pZdum0Jbr/nScanqkkMPowUYRhiSRgo2DiWhZvKMDZVJVK0iffEuYEIgWCg4Latxjl8ZJQHHzvJYMFluuiBgGo95PR4mXItSO431Jfm+qsv5Nd/7TwG8umWEtCtgHEEBoOhK+5++HjDCWgDZwsIooi7Hz6+qCNojt1Hi9v9FmxLcPn+Qe5++DiVmo9S6ASvgNlyXf9s6XBNvOsPI5ia9RBCYNuSjGvrU0RTHiLnWlTrIZECFcGuHRlyaZtaPVgQcopPM7lMCtuSTBdrifOJX9tvvm4/r3v5bvoLLtlVqoBuFsYRGAyGrqh6AfOjHJYgmZDViYPXXsKtd/6MsalqEkNfDCkE2YxNxrX4+//vFIim2P68x7er+IkUpCwIgohsI4xzdqqKAGxb0Jd36QPGGn0JWdeiVg/aVuOcmSjjOhZnJiotJwBLCn7nXS/jNVfsppBPLVoCuhUwjsBgMHRFxtW7ZrvJ3oVK374U2kh2ZyiFhGotwPOC5AQShuHSD4wfDygEKUcipWC66OE6FmEUgRKMTVcZHsjQn09Rq0eUqsGCapwwUjz6+CkqVb+lC9h1JIWsw0vO7+e611yw5iqgm4VxBAaDYQHtOn3f/caLuOv+pwmiSJdfNmLv737jRYs+vlz1Sack+0byvDhWph6Ei9buqwgcW+A1RjEuxwmgl4RSMFBwGZ+u4AcKq1HxI6TCEoLJ2RqDhTT/4YOtSqNBGPHQT1/gOw88zanxOR2glC0ZHkzTl0sBgne84eJzxgmAcQQGw7anXafvg4+dXCDV8PGDB3j/dS9dsmqouVxUoihXfcpVmJjxulpPpBSev3STV6d7CGB4MAOAH+j8gSVF0g+gUNhC8PEm6QnPD/nhP2kdoOb+g1976TCXXzjIc2dmmZr1yKYd3vGGi3jlZbu6ei1bBeMIDIZtzOEjo9x658+oegFhpJgp1njy+DiDBZd8VmvpNAud3XLTNV2Vi/phxMRsfcmqoJWyVKZBKcXUbEPXR+rQlC20Y5ISXnrhEFddMUKl5vODfzjBoYeOMlOq6/sLXbv/r1+/n0v3DpLNbI0S0NVgHIHBsI25/Z4nKVZ8ZCPRGYdjJmY8Uo5NLq1NxGJCZ80nioxrcXK0tCwdn7VGCDg7VUUKGMinmC3XqQdh0lOgQnjb1fv5n3/3FH/z98cpV7UOkCUFV7/iPP711Reyf08/2YyDvYVKQFeDcQQGwzbm1FgZ2cjjBo0wSmzC57p/Jdm0zZ7hwoLHN58o/CBiE+1/gpuy2NGfZqpYx7KE7iCO5jqIU47kj+76GfWG03Nsya//2vn85tUXsnekoHsAtmAJ6GowjsBg2OYoGpIL0BJ8V+jqmaoXUvVCLCk5fGS0RR76y3/5GF49XDRmv9HUfT2jwLYExbIPAlxHoBDU/ShxAGnX4tpX7uX61+7nvJ1ZMvN6ANZyHGWvYxyBwbCNOX84z8nR4pwUQ5M1T4TYhK7tnyl53HrnzxgopJkuepSq9aRRq1ecAGjndeJ0kbRrY9sCEcYhL71KKSCbdvjy776BXUMZ0qmFPQBrNY5yq2AcgcGwSrbazrF5vVnXJp2yqNSCFmO+cIev8ANFEPpUvZBIRV0Nddk8tGx01ZsrPbWkYLDgsnMgzY6BLBee19fx0asdR7nVMI7AYFgFvbhzXGraV/N6PT8k5VjkMg7jMzUcSyyI9SulewaEaMg3R6pnnYBAy04EoWrpeB7qc9m3K0+kFLNln3/16gsWvc5GTQbrFbZHStxgWCead46xvr5tCw49dHRT1hMb+qnZaotjOnxktON6cxmbkR053vhre/BD1THhG4+JlD1oNYTQO34FiZNyLMnuoQwX7Slw2f4hPVVMSH77HS9b0kmPDGXx/NZGtvWYDNYrmBOBwbAKem3nGNfwz5R1FY9j64qfOKTRab3HX5zhF0f9JWP9zcNceoE4fxFGirBpYecPZ9k9lKNaD3BTNjd/5DXLuu5GTQbrFYwjMBhWwUbNlO2Wk2eKFCt1pBRYUksmTBe9RNKheb3lWsB00aPuh12JwUFDvqEHUsNCkJSFNjuArGtTyDns7E8zPlPFq0d8/OBly77+Rk0G6xWMIzAYVkGv7Rz9MILGLhl0hUwklL4deMXFO/jOA88Qhiuv+V+OlPRaIwQI9GQx1XAAAj3AJp91qNQCxmdqgFi18d6IyWC9gnEEBsMq2MydY7uksG3pgexRpFomdNmW4PCRUe599MSqnMBmIcWcmFx8IhECCtkU2bRNqeJzdrJCpOAD11+2pAyGoRXjCAyGVbIZO8dO1UqDhTS2JajUmnMEDnuGC/zFPb9kpuRtKScghUDRmsCWUtCXdUi7NqVynTMTOh/jOpLXveI8njg2wQ+/dP+WKOXtFYwjMBi2EPEp4KkTk0ghGOpPJ9U/NQIUCsey2NFvLwhV3XL7T7aME5CN00zUFP+3pKA/ryeFlasBOwcc8hkHpRSlasAnDh7ouVLerYJxBIZtzVZqBms+BSilk6RjU1UY1KMWXcdKDOL8UBWw5KD4XkAKPWGs2WHZlp4pDFCs+HhNoyLLtYDJmRqRUnzlr35KOmWRz+r7nutNYGuJcQSGbUsvNoMtRnMPgGNLgkAnAKaLHrm0zXSpTq0ectuhxxkZyvKJxuuIX2cv6QHNp50DcGzJQN4ljBSzpTr1eZLWpabpYcMDacama9S8QDfItVFNbTd34YljE1tiE7DebJojKJVK3HDDDfzpn/4pe/fu3axlGLYx6yUjEBuc58/MEoQKx5Ls211oa2iWcyJp7gFIOxZTntbP94OIs1MVytWAgUIKieLp5yb54p//ExeMaMVQ2xY96QSk0BVAzQ7AdSz68yn8IGKq6HU8yUzM1LAtwVCfnpugGtcZnagwMpQhl3GSUt75Tv/UWIlfHp9goJBiIO/2/CZgvdkUR/DP//zPfP7zn+fEiROb8fQGA7B0M1g7Iw0sarhjg+OHIeWqbtCqKTg1VlpgaJZ7Iol7AMJQUaz6SRxdAbNlHyFgtlwnirSBlQJeOFskCFXPnQbmTgBzq0qnLAbyLtV6wMRMbdHeBilgsC9NIWNTqQWMTVe1cJxSREoxNl2lHoQ4lsXBay9Z4PQrNT0PuVILGCykt30YaVOaxb/97W/zhS98gV27zq1xb4atxWIyAu2kGr5218+59c6fdZRvgLlTRqUWIBDYUg9Qr9SCBdITy5WnOHjtJQSBYnK21tDUkYm0Amin4AeqoQUUoRQ9pw4ai3w22/isazMylCHlWJydqjBTqi/Z4ObYkqnZGs+dnuXMZIUg1A4g7jRWCmr1KBlHOTpZwXWs5PF+oOcuN09QO5e1hJZiU04EX/rSlzbjaQ2GFhZrBmsXNhqbqgGKnQOZ5LYpr8aX//KxZPBJGEXsGsjgB1FLU5cfRAsMzXLkKeLTSa0e6li50nr6Quhh79G8ft94lkCvEPc0xAcAAWQzDrm03tGfnap2JV1hW4IwVOSzKcIwYroxXhLmrj1QSDHU51KqBsnufn4HuGNL6kGIJQQvjpW0Y5CC3Ttza/mytwwmWWzYljQbVr8aYVuCC3b3JaGe2w49vsBIB2FEs2x9uepr+YaIpkYuGJ2s4thSN3M1QiCOLRdIT3SSp8i4Njff9kjbYfJp12KmpA19rR70lO5POxY4AAH5rEMmZVOq+pydqi5+gXmEkaKQcxgsuLw4VsJqOIb42lIKavUAz7db3uv5Tj+btvFmA3ylUEr/UYNQJ6Wbh+9sF4wjMGxpVlL+2Ryb39HvtpwEOu0ggcb82jnLO13STkCHZ3SU1SIiDBV+EOl7hnr325dLLZCeaHciKVcDlPIJwhAp4MjxCZ44Og4snBHQy05gvgOQQlDI6SHw5WrA2fLyHECMUlCq+OQyQSO8IxCWIAgjbEsmMwheHCszNlXlw1/4QZKo/3hTWe2e4TyWFEkYyrElAwUXS7It8wQ9KChrMHTHUpLLnegmNh/H4/WuW/83m7bJuHZyWzzy0GrWZVZzMXkptPGOd8Efn5cEvuqKET5+8ACDfRlK1QDbsqjUfIoVvVMenagQNIV3etjuJ8QHptgBxMNghvpdavWQ8elay5yAlRApXTLbfOpKOTpfEkY6MS6AIFIUK/W5RD1wy03X8I3fv45bbroGhGDvrhz7zytw/nAu6cXYjnkC4wgMW5aVzgKYnziEhbH5+UZ6sC/Dp95/JZ++4ZXJbWnX1iGhpnBRLMiWcW0uOr+fi/f2s2c4R1/ObbvLvOqKEW656Ro+cfAANS9oJHe1k9kKhj8mcQCN/8Zlnf15l3LVZ3y61tIItlrqfkh/LqVLTyPFUF8agcCSAtsWWFIumqiH7TdzYDE2NTT04IMPbubTG7Y4K50F0K10dCcNoebyz6/d9XNmy3WUmCvRFOik8bOnZhvhEcWLY2U+9IV7W/IQzcROTUrwV7dh3hRiB+DYkv58Ssfby/V162ZOuzYKwd6RPAJB1QuIlGJ4IM34TA2lIoJAn0yCICIInAWfi4PXXsKtd/6MsakqYaSwpCDj2nz0Xa9YlzX3MuZEYNiyrHRH1y7ssxLp6KuuGOFT77+SfSMFXcMuBI49F6III13SGKtmlqs+p8aKbcNX8SlFINo/WY+TciTDA2myaZupWY/pRRrB1oKdA2k+cfAAf/zZt/D1z76Zb/z+dVy+fwg/jBrv/Vx4SgFj0zXSrrXgOnpovWjcVywYYr9dMI7AsGVZqUFvF/aZH7/vlquuGOHrn30zh/7wnRz6w3eyZ1iXH4ZtZvoGoWJixmN0qsJt3/vn5PbDR0YpV31OnC42yk7ZMu7AdSyGBzOkUzYTM7WuegBWy0DeIQyjBQ71FRfvYLpYX5BEj237fCd76KGj5DI2+0byvGRPH/tG8uQy9qaNGd1MTNWQYcuymlkAaykd3Vy5NDFTXVLhUyk4O1Xljvue4tJ9g9x658/w6nNTwnq5GigmnbIoZFNUvYDx6e56AJaLaMTahNQieylbMtSXJpdxABZ0Aj9xbIKBgsvkTK0lvyIQDA9mFiSpe23M6GZiHIFhU1mt+udmT5G6476n+O4DzyQliO1OAvOJcwl3P3ycbNpKmqJ6TQZiPgLIpG1yGYdK1WdsemUloN0ipcASECqdhM+4VuIEQBvt58/MJj0XU7M1dvSnSbs2QRg1Oox1eM62BIN9mZbr99qY0c3EhIYMm8ZKyz97hcNHRvXYR6UTjd04AdDG3pI6Z6C7ledu70WEgHzGYedAGqVgbKpKubb+GW1bSoTQ1T8AMyWfF8dKnDg9y4tjJUanKlRqQfL5kUKvLeNaySwD1fjbtAsZrlWu6FzAOALDprHS8s9e4dBDR4kihS11klHK7iL7ArbEgBg9CtJhR1+aIIwYW4MegG6esx3xqMp6I4dSDyJKlQA3ZSWfn1iFVA+tSSOFSMJt1boOIzVvMtYyV7TVMaEhw6pYTWhnvWK06zlspllierZcT3ae1jKqTVTyr95ESkEh62BJQbkaUKz4G/K8ltQD58+Mlxe8PVFj9nLKtvCDiJQt8aKAoEk0LpdxGFaKiVkPpeC8nTmmizXyWQfXsdqqu252aLFXMI7AsGJWO9hlPWK06zFsptn4V2p6F1rz5nR+lIJgK2R4l8CSgr5cCtBDX/xg/SeauY7E87WGkwImZ6qkHEndjwiiKMkRgM4RNIpxAR3ymb9G25Zcvn+IW266hptve4QgDNd83sS5iHEEhhWz2sEui6l/tqObnf5aDJtpfp6MazFbqpPL2Hj1kCCM8Cva+HRK7sY6O1sF2xIUcimihiRD0GWuY7W4juRzH3lNoynPI4wUXiOUc+HuPOMzHlUvIOPa2BJK1aAxZ0HH/MNIy07X6u0/P6YqqHuMIzCsmJV+0ZoNbda1Uejh4/PLPzsZ5MV2+qtd08kzRSo1n0LOYSDv8sLZEkGoSLs2dT9sMfCdzOVWcQKOLenLpgiiiJlSPZHS3igGCi6HHjpKGEXEvWexc33uTIkPXH8ZN771cgA++ZUfUaoWQZCcHoQQ7BxIM9iXaVs+bKqCusc4AsOKWckXbX7oJt7FfWKeQZ9/v2aDLIQgjGCq6HHL7T/h8v1DiQHIuBYvnC3NKUrmXSxLJGtqN7f2kcdPcfJMMZn2BTAx4zE54yU/nx4vd/WeSDmnN9SrpGxJIZei7odMFWsbnriWQtCfdwDB6GSFYlnnIJI0S2M9dz98PHEEFS9geDDDTKmOH0RayqI/hVJaSK4dyz1xbmeMIzCsmJV80boN3cy/XxwGmC56AI2h5dpixKeDZ169T4ubNVRBgzDEq1dIpyxsy+LDX/hBy27/1FiRXx6fANpX8azEPvayE3BTFvmMQ60eMjlb29CTS2zjLUvi2BIpZeKcz05WFnRSC0FLhVK86Th/eG5wTK0eLOgNaGY1DYfbDeMIDCtmJV+0TqGb5sagrGvz/OgssqHdM5B3cWxJEET4QcTETJUw0qMYpdByDrYtOPSjo1qj3hJEkWpU9OhTShCGuk48ipiY8ZiY8RasbavF9rsl41rk0g4VT88C3kiG+lxUpJhqNM1JoagHIfViyPVXX8il+wb5xdHxBZVUSkE2Pfc5Wenu3lQFdYdxBIZVsdwvWrtw0nTJSxqDpICTo0W9sxaKINCDyAsZh1k/QghF3W+11mPTVfIZm1pD5lgIsITAsiV1PySMYGKmlvy+E+eSE4i7gDOuHgU5vsEOIGZq1kuOA0JAPVBIAdmMzRPHJrjxrZfz8ouHeOLY5ILHvvqKuZnmS2061rNkeDtgHIFh3Wj35Wy3syuWffpyKdIpmxfHyroxS0AU6uJxoXSM2HVkizGXgGNb+EHIdGmu1n2unHNO07/qrZ0Wfi8jBGTTDq4jKdc2/gQwn+advpaMaAyW90KePzMLQLESIMVceE4I3RMwMdt6auu06ViPkuHthuksNqwLneQjgAXdnNm0w0Be16/H6ptWo1PXtiSRAj+McBsdpFajgzcCwnCukse29O1xvLmXhrevN7LRBTyQd/H9kMlZb00HwSyF1YUliSKVdGArtBrr4SOjPD9aREqB6+j8gSUl+czC+QGd2Ood6r2AOREY1oXFksK33HRNy07t5tse4dRYiUotIAx1KaGUgpQjOX84R60eMFXUpaO1Ri2/bDQThdGcdo+UuvsoNv/bwQ1IKchnbKQQlKoBQbj2XcCxCqh+n0VSkQW6q3q4P83o5NICdKpxPIgaPWGOJTn00FEcSzSS7EKfDFBMzXq89MLBrtZn+gVWj3EEhjXljvue4u6Hj1Ou+kgB/XmXHf1aA6bTl/MVF+/Q1TtCl1+GkU4AuxkrEQJzLInrWAwUXM5OVoiaZwM3ZIrXcxBKr2FJQT7rgNJdwOt6+mkK2Qih+w8O/eE7Ae3Ep2aXdgJxIt7ztSpoPmuzb3eB0ckKgwWX8ZkaURQ7HUU9iCiW63z0S/cvGfPvtozZ5BE6Y0JDhjXjjvue4q77n6ZW12V/kdK1/nGculOPgdaRT5GyJUIIUrbEllCs+kwV61TrAX4YMTZdbVQMqSQcJARJI5Rqako6V7EtwUA+RTZtU6z4zJTXfxBMLOqgHS6cP5xPfhcreFrW4u+6UtrJp2yBkFoY7hUX72BkKIttS4YHMti2SBRDpSUIwrArVdpuVES3utLtemMcgWHNuPvh4yC0fLDTFDSeKXkLvpyHj4xy822P8OEv/IBfHBvX1SXA8ECGC3YX2DmYRSkYLG7g5AwAACAASURBVKTY2Z9GCJgt+9T91l3/QN7FknN5Azg3Q0KOLRksuKRTFrPlOsWKv6GdwLLxd+3LpfjI2/9Fcnus4Ll3V2HJa9hS53tStmSgkOKJYxMtjmTPzhy7d2SRUpcMdxvz70ZF1OQRFseEhgxrRtULaISOkVLgIPHDiEjBYF8mOYrHw1yCxu9AG++qF1L1Kji2RCmFYzU1lIWqpc4/DlPU6iFOo0y05ye7rICUI8mlHepBxHTJW7cSV1sKgnmOZTCfQlqCWj0in3E69onE1TyHj4xy+z1P8nyjS1s0Rm7G/29ZgguGtcNQSnF2stK2LLRUCZLigZilYv5LlTGbPMLiGEdgWDMyrk2tHmA3NudSCiy0Mb/lpms4fGSU//CVB3nudHHR68SKkmGkeHGspCthggjHEviBwrZkQ/tf4QcR2bS1ZI/AVsN19DQunShf2Py21gix0IvOVnwKWYfPfvBVgN5V33bo8Y7x9fjn//qtf9JDehQIqUOEStFSxdQcJpxvxOO8w1pqBBndocUxjsCwZrz7jRdx1/1Pt8oHK317EqMtdl/XrtTcKQH0rtK2G+MHI1AoLCmpeiH5rEO54m/5A0E6ZZFJ29Q8LQOxEQh0eS7oOL5qGO4oUpSqPrff82RXgn/x3zgK5xL5ugKMxt9LnwTadQXPFxgsV3Weaa00gozu0OIYR2BYM2KBsLsfPp7IB7/7jRdx41sv5+bbHsG2xaoSm0o19HLSDlNFDz+EHYUUY9NVqrW4Rl3HoutBuKU6hTOuTSZlUfaCJF+yEViNKq34/6WUWp6jUeYZhIrnzxQRkAj+xaXAf3HPL1uqcIrlOrat+wTCSCWRulgKREjRUWV2vhChUgrbspiY8fDDCNuai+evpNLH6A4tjnEEhjXlxrdenjiEmMNHRnnqxCRKqVU5gowrUUqgELz0wiFecfEOHnzsJI4l8YNGviHUTWbz6VUdoVzaxnEsqrWAyQ0IATVjNSaR/etrXsLdDx+n5gXYlsRX4ZwsdON9U8DETJVcWsf4g1BxarzInp3Z5JRwarzCrsEMKceiHoQ6md04FQ72p9kznG+rFNqu5yQmnbLI23bHCWPLwegOdcY4AsOK6aYuO97tSSEIlVqVMfYDRS4j+MbvXweQnDKyrs2UX1/0sb3kBISAXNrBtiTlmr8hg+DbYVmCUsXnOw88w57hHONTVYIwahlMYwlBiP67+U0VW1OzXpLML1d9pkseUaQYnagw2KdzOnZD/C9SiqmiRz7rcPjI6ILPSEchwtESfTmHmXKQSE9n07aZMLYObIoj+P73v89tt91GEAR85CMf4YMf/OBmLMPQJe0MPtCVvku82xvqT7dIR7dDSrFoSWQQKooVnzvue4onjk3w5PEJUrYOZcQdr72MbGjoyMYs4PXoAl4OcSmuVIpSpU7VCxpJ+FYsIQiU7tg+cXoWSwo8P2Ig7/D8mSL1IEIwV7Q1U6rTl3MoVnSjmy1h50CaMIzafkY6JXIjpZgp1RHMTSWbKdUJwtn1fWO2IRvuCEZHR/nqV7/KoUOHSKVS3HDDDbz2ta/lkktM0qYX6STo5brWvHkB7QfFxLs9IQQMZhYd8PJ//85rOfTQUU6NFdvKRINOYP7Pv/tVstuMq4XsJgPWa2EgKQX5tIMQG9AFvEIqtUAL/c1rzg6VmhsYg3bG8fpnSnPJ+fi/Uuqmt9mKr8tGAcexWnIL8Y5+/izovlyKgXwqSeTGJa1xj4gQEERqw0Zpbic2vKHs0Ucf5eqrr2ZgYIBsNsv111/PD37wg41ehqFLOjXinBor4zoWAOVawNhUPCNAtXRtjgxl8XxtrHPpxfcdV10xwsFrL+lquEsQqkRkDmipgbe7UUDbACwp6M+lyKVtSrWN6QJeCVGjOiuKtHZQ86FAVw/N/SybnGz8SuLTAOj3frAvPecEbJFIiZerflK739zpu7M/rU8Q5ToTM17SEJZxtYSGlhPRIaZYo8iwtmz4O3r27FmGh4eTn3ft2sXoqGnz7lVGJyuJwY+Jf44N/HTRazQP6UEyzV2b89v/7XlSBLERufC8uc5Ur95dzLzTzjDuQ9gsbEvQn8hAbHwX8GqJGlU+nX43HwW6o7xxSovzBynHQqGriASC6ZKX1O7P32AMFtLsGsqwb3chESXct7vAQMHFtiRhpJ3MQMFl3+6lu5gNy2PDHUEURY3mFY1SquVnQ2/RvKOP8fyQ84fziYGv+yEoXXI4kHeBua7N+e3/e3cVyLoWriOxLa0w2p9P8dtvfxkAt9/z5JZtDtOGKoXrNMlAbB3730K367YsgUC/doE+BflhxGDBZaDgEoYR9SDEDyOqXkip4nPw2ks6bjCaO30PXnsJjiXZ0e9y4e48O/pdHEsuu/Y/ljP56Jfu5+bbHjH6Qm3oeFb/8Ic/vKiB/su//MsVPeHu3bs5fPhw8vPY2Bi7du1a5BGGzeQVF+/gOw88Q9SQHs5lHBxL8tF3ab0ZnUTWk8WG+tLkMg6weOdoHBtuV8/9wtnSljOe8ftS90NmSvWeyk8sl+WqdAh0f4AUOp6/Z6eeKRyGUUMWZK4AQEBiU7rp9F2L2n8ztKY7OjqCD33oQwDcf//9lEol3vOe92BZFnfffTd9fX0rfsLXv/71fP3rX2dycpJMJsN9993HF7/4xRVfz7B+HD4yyoOPnaQv51Cu6hK+YrnOe3/j0uRLFCf9br3zZ0zO1hibriYicLZltZURjv8bVyI1NwqFWygRmOgA+SHTG9wDsF40v/sCneiWQiSdxzGWFPTlUlRqAUIpXnrhYPI3bu4ilwKkLVEKhgczWJIkZBh3+gZB1GgQ1E1kzSWmq639X2wuhnEEc3R0BNdffz0A3/zmN7nzzjuRUkeRrr32Wt7//vev+AlHRkb4zGc+w7/9t/8W3/d573vfy4EDB1Z8PcPy6VaXPf4S5bNpBhth2Vo90LNm591X7/RE0jTmBxHFisdA3k12Yc+8eh9PHJvg5JkilZpPIadPF08/N8kXv/mPWFskCeimLLKuHpKzETpAK0HvvvX/xw1hy8GxBPmMQ7GqS1ybO5D1NRWVmk/Gtfn0Da9s+fxcdcUIz7x6H3fc96ukq3igoJPm88Xmbr/nSU6PV7Atya7BNEEYrumO3YjNdceS5aNTU1N4nkcmkwGgXC4zMzOzqid95zvfyTvf+c5VXcOwMpZzVO7Y6HNmlptve4TRyQpZ1+b0RJkgiEg5Frv6s0wXPepBSKUWMFhIk07ZTHk1vvPAM4wMZajVA0KlmC7WG3IGmmiTk7xLkXYtLazn9a4DiEmkHRrifBLtGDrN7mkuuXUd3ak9U643wnQquU/KloBoDAESbcPH8Uky7vFACGbLPm7KxpK0hAwPPXSU83ZGLSGitdyxG7G57ljSEbzjHe/gfe97H9dddx1KKX7wgx/wvve9byPWZlhD4lPAUycmk3i+SHWu7R6drFCu+oRhSMqxmS56uhqnURc4NavzAs+fmU1i+lUvoOrpih8p9IDyE6eLOLYkDCOiSJFO2br0s9GUtBXYLB2gbojNcFLHL0QyvS2uv3dsKxnvGXaozbWl1PexBG7Kavu3kUIQhIpdQ5mkFLhWX2i0kybCvjRj01VEw5lMztQYLLgtyd713rEbsbnuWNIRfPrTn+blL385//AP/wDA5z73Od70pjet+8IMa0OsEX9ytIRjNQyyEJyeqCAFuCmb/nyqpbY7Pi2EYcTUbA2lvKQ6xI90nXkYweRsrWNiN77dkjRKR/XPR1/Qp8lenyopgExaa9xUNkEHqFscWxI1wnGWFOw/r48Xx0oEgWqUdMpkvOdiZbVBFCGEHvRTqQVYlmjJ11iWIAr1AJnpopc4gnZGO2kiTGlHNF3y8APdBzB/YMx679iN2Fx3dNVZPDw8zCWXXMLBgwd58skn13tNhjWiOWlnCd2YE8UxA/QuMggjxqaq7BspLEisDRbcZMxkXLMfJxCni96CaWHt8IOobRVNr1bWCAHZtI1jWVRqvu647WFUU3dXFGlNn8TgK8FAwSWXthkezHBmYk7OW4pWSY6UbeEHesiPH0TYcp4jEIIIrSja7FDaGe1m457LOMlchcG+zAIDvBE7diM2tzRLZue+973v8Z/+03/iG9/4BsVikX//7/893/72tzdibYZVcuiho/hhRN2P8MOFw91VPIwWre0/v7Z7YnrhUHKFNjjdNm1tlVJQISCfdRjIu9R9PQ2s3sM5C9sS2JZIGrYcR1fmxLkLS0qGB+dCOPH9dTWXTMJH+r6CfSN5HFvqZjBbj5RsDv/Xg6gxX0CfPDrNBobuZgjHdDNm0rD+LHki+B//439w11138aEPfYgdO3Zw6NAhPvaxj5k8wRbg5JkixUp9rjh8nlEW6EEv/TmXmhcuOKbPlOcUPZuTiZEC1UVsp9c0f9oRC8FZDa380iYLwXWLm7IIQ+2QfV8Pe3dsQRhBPuMghMCSrYNgzt+Vp1TRpxw/0KEgKUUjAaxPgGPTNQaybttyWClBRboCqN1cgZjlhmPMjn3zWdIRSCnJ5/PJz+eddx6WZS3yCMNm0pzsna3UUU3Sz8022bYEwwOZlmP7/Nru5t38fIPejX3vZScgpWgYTChX/S0lZCalloQOlY67K/RMZ6Tu1M5nHSxL0pdzWwwxaMXYHf0u49MKP9SqoXE3uG1L9o7k6cu5hKFKPj9SCF0x5Fhk0za7hnJt5wo0Y4z71mJJRzAwMMCRI0eSMrG/+Zu/ob+/f90Xth3otp5/OddrTvaenexs3GItnOZje3Nt94tnO6uEdkOvzpG3pCCf1WJmvaoEuhRRBIWCs6CLOYy0xIfrWJSqAX/82TbGurFTL1UCokYvR/Pn4KPvelnyGfzol+6fU45toJTi5JliUj68Fp9bw+azpCO4+eab+fSnP83zzz/PG97wBlzX5U/+5E82Ym3nNCtpfV/KccxP9jaXEsJczDdlS/YMF9oe26+6YoTb73kS2YgDd2Mn2z6P6s4Z2JbYkN24ZQkK2RRRFG05Ebh2DORdUrbF6GSVqCEVLSA54XWqumneqS8m9QHtK3qmS3UqNZ+p2aqRbDiHWNIR1Go17r77bk6cOEEYhrzkJS/BcZyNWNs5zXJb37txHM012eWq32KcExRkXIdbbromMQS3HXq8xbGcGitraQBLV5IsZTNHdmQZa0y3gvYhobgaZT7N1UjrYZptS1LIOQRBxGy5vuUdQMx0ydPyDoJGnb4O3SyWmJ3PUuGbdhU9s2U9dMZINpxbLFk19NnPfhbLsrj44ot56UtfapzAGtGN+mIzneYCxDo9h4+MUq76nDhd5PkzRUbbXEc1KkH6C6kWPfhmxxIrMzYPHFlKG/bMeLmlIml+s6kQS0tDr7V5dmzJUJ9LNm0zU/TOiVNAjBAwXaxTDyIsCULODYhfy6qbdhU92bSd5BRijGTD1mfJE8Fll13G97//fV71qleRzc4dNwcGBtZ1Yec6y22kGZ2sIFG8OFNK5rf251obwdIpi5oXLFr2GCkYnahw+z1PdjyRnD+c5+RoUSciu7CditYKIdmYXpIMQGfj8gUpW1LIpRIhuHPE9rfgOha5jJNU/7iNJG6n4fCrYf6p4ebbHjGSDecgSzqCBx54YMEEMSEER44cWbdFbQVWm+hdbiNNxrV4YbSEFHPzW89OVXFsyS23/wQp9FzgXUNZzoyXE8NrNeYAN9vDWj3kudNFBvJOyxc63tl94uABvnbXz5dsppJSlxxWajocIaXAD0NUpD8jbkrSl3OYnPHWvYQo5Vj05RxqXsjkbK2nK5Y60a7Rqx1+qDcC5w/nkts2KolrJBvOTZZ0BE888cRGrGNLsRYa58uttRZxgEY0xMMaiVy961aESjE2VWV4MEPatfGDkDDUxjke8Zdcq7F7ny75VGp65y+llhAQUoeb3vb6/Tzy+CmeO11sux4poJBNJUnLsekqUaR3/2Gj5PC8HTk+8nY9t+CL3/zHddmduymLQsahWg+ZmOk9BzBfqqEdg4UUtXqktX6EQIioJYHenFAfyKeoeno0qBAiaRjbqCSukWw4N1nSEYRhyJ133smPf/xjLMviLW95CwcPHtyItfUsa6Vxvpxa64oXMDyYYaZU17INkd6Rgx4YE2vLTBe9RFvGsrT644IegKaf60GEFHNJ24GsNiIPPnYStVhARwgu2N3HqbEilZrWEgoagmYC2DWYoVipc8vtPyGbttkxkGZsqtb1e7MU6ZRFPpuiWvMZn1m7664lg4UUlVpIGOqJa1abSqyRoUxjIHxTk5cQuI4km3boy6ca86ElQ/1pcmmbctXn7FSVyZkaWTe37CTuak+zpkfg3GNJR/DFL36RY8eO8e53vxulFN/97nd57rnn+MxnPrMR6+tJNkPjPM4pxOGAE6eLjc5gyUDeTVQe636IJaEvl6Ivn2KmWGe2sni1TPOvpks+5VpAXy7F5CJKmxnXZkefyxNHx4HWBLFlicYwe33hmVK93SVWRMa1yWccyjWf8TYSGL1CLuOQcmz2jvTxiot38LePPEvVC6gHc292OmWxazDDL5+dAjGn+R8qxY7+NDe9519y1RUjC+r5cxmHYaWYmPWSDt9ixe8qibva0+xa974YeoMlq4YeffRR/vzP/5z3vve9/NZv/Rbf+ta3FuQMthud5viuZ8Jsvn6LJQWR0vXkuYzD8EBGT5OSgsG+DJ96/5X88WffwqdvuJJ9I/mln6AJP1BMzHiLhllefcUuHnn8dPLz/Mam1TRqza9SEkAubbNzIA3A2HS1Z8XgpNT/DA9kkvPUpfsG+fQNr6Qvn0rep3i27xPHJpFSoSLtBKTQDW8jO3KJgR0ZyjJd8nhxrMSzp2Y5/uIMo5NVUo7FJw4e4JabruGC3X1dfSaXqj5bjKUqzQxblyUdwdDQUHK0BZ0EXM2oynOB5YhqrRXzS/nO25mjkHWwLB1usCzBYCHNzb/9Gm656ZqWkYHhOmg+/+MvznQsCV1pnH6+tn4sBLdjIE2kYHy6lsw76FUEuk8jCMPWHTdQqYVYlsB1LGxLYjem/vkBjdslliWoVH1++exEMmx9R5/LdLGO54eEkQ4tKaDmBXzxz/+JT37lR7zi4h1dfSaXW7bczGqciKG3WTI0dPnll/OBD3yAgwcPYlkWf/u3f8vg4CDf+ta3APh3/+7frfsie43NSJi1O5KD/nKePFPEDyNsa+5LGU9/ir+4Gdei7kdrJqlQq4dL36lL4uR1iwPIOA2pBJ/xytYQgpMC9o30MT5T4dRYmahR25/N2Bx66ChVL8DusPWSYs4NRgpEpBJH8qsTk+QyNqVKQHPWP4wUtiU4PV7mwcdO8pbGKND5+kLNlUQZV1f6rKT804x9PHdZ0hF4nsdll12WzCHYu3cvAE8//fT6rqzH2ciEWae47scPHkjK+fK2HqLSHPNt/uLGeQQpekcaOnEAcf+BgHw2hWNrJdDiFnEAMKdhdHayTMWbc5KRglIl4MjxcaII6hFAmMhBJ/eLVEvjnZ45rJ14GCnqvp4w5kihJaGbQkxhpLBtwRPHJlr6CNp9bsrVIJlhsNzyTzP28dxlSUfwB3/wBx1/9x//439c08UY2rNYlRKAH4bMlIOk0Syb1jvQ+QNCal7A1BombldKOwfQl3OREsrVgNny2p021hurofipG7oKPHlsvO395kfRdB5l7nUGYTSvRktQrgXk0nYiz5Fy9MjJ+H0TDafu2LLtzrzd5wbAtiwKjWbE5ZxmTQ/BuUtXE8o68eyzz67VOgyLsNiRvFYPKFd9BHONZjOlOkE4y/9xwytbZKVnGzvseJbtRitvzncAlhT05VIIIShV6j09CKYTerce4gcRQTi76GnLloKgwx0W3Cp0XwiDGXIZm9myTzZtt8wJ0MKAMFBw2+7MO31uStWAr3/2zct5mYDpITiXWZUjMGwMix3Jj784g2LOuAsBQaQIQtXyxX3qxCS2JbEs1dClEcDa5QwWo50D6M+nAEGxUu962lmvEkYgUGRce9FS2bDxBsTqrIu985aQqKaB77/1G/t54tgEYaioej71IMKWksE+F0vSdme+HqEc00NwbmIcwRZgsSP5rXf+nJrS84iT+L8CpxF/jr+4cS16xQsZm6oSoZK69fWguQIodgC2JejPu0SRYrbsLxidudVo1leybblkv0RzNdWS7ldoT9E88P3Gpl83S0jHQ4U2Yx6w4dzAOIJNYLlNOYsdyfftLnBqrJQIkDm2JJvTAmTNzzU1W2OmqPWIhgczTBc9vHqwbuMkmy/p2JL+fIowVMyUvC01DaxbZCPR28372c37bVuSHf3ptgPfobuduQnlGLrFOIINZqWdnZ2++PGub0e/u2DX1/xcO/r0PNqzkxWGB7WccN0PGSzoDuLVOoN2BjDVcAB+qJgunoMOoOnlxEnbwGs95cQDY+ZH4AYLKYqVoOOpqO5Ha7J7N6EcQzcs2VD2yU9+kkcffbTt79R6bCXPcda6KaedZvzHDx4A4Ct/9VPGpqtMzNQQQrBrMINtCSZmatTqIQOFFIOFNOmUhVxq6EAH4sc1fxRSjmTXYIZs2mFy1lu2E1jhUjac+BVJocs/+/OpRGrDsSSOLRvhHX3b7h1ZdvSncWxJpOD8XbmO77tjyzWbK2AwLMWSJ4LrrruOP/mTP+G//Jf/wvve9z7e8573JLMIvvrVr674if/oj/4Iy7L45Cc/ueJrbEW6bcpZTvho/q4vPgnUvCBJJI5NVxkeyLB3V55SNUBBsg49anJ5ryMeT9n8ODdl0Z9L4fkh4zO1ZQ2CEcCuoQyOLQkjOD2+upnJyyGel7BYzqQv5/CZG18FaGf+/JlZglDhWJL+QgqBoOoFDA9kGJ+uEqqIpupQhNCd0cODGUaGMgz2Zbjlpmv4nf/6d4kYX3x6EEIwsiNrnIBhw1jSEbzrXe/iXe96F8eOHeN73/se733ve7nyyiv58Ic/zIEDB5b9hMVikT/4gz/gnnvu4WMf+9iKFr2V6aaSY7XCYPGpI647l1IQRXq8oWWJ5LmmZquEEcvS7YkbmJrHYKZTFn35FDUvZHy6uqKGNQWMTm6OiJxtSSKlGOp3KZb9tl3TQ02x+qX+Bnfc9xR33v808ZlBAo5tESkdItuzM5s4fiEku4cyzJTrLQOHat7W6aUwbH2WDA0BRFHEc889l8wt3rFjB//5P/9nvva1ry37CR944AH279+/LaUpoDudotWGj2I9mYGCi2pUngihWuLO8TomZ2pdTQ+TbXoPMq7NSGMXPz5V1TOB1ZwSaa+HePTuW7822Rj/GMs2uI6V/GNbghfHSl1f98a3Xs7OgQwXn99HxrWwGhVccUK52fGPDGWxbcn5w3n2n9fH+cN5bFuabl3DhrLkieCrX/0qhw4dYt++fXzgAx/g1ltvxXEcKpUKb37zm/nUpz61rCf8N//m3wDw9a9/fWUr3uJ0U8mxWk2X+NSRS9vQqBCq+yFp126JOz/z6n3ccd+vWh7bPFZSoI1kGKmWME82bVPIOpSrAWenqh3nHfR6BkkBTkP4rXk2czv1025oDueVqz5hGCXSHlEECoUlZYvjNyWehl5gSUcwOTnJf//v/53LL7+85fZsNst/+2//rePj7r333gXyFBdddBG33377yla6AWyU1vpSlRyrbQRqNi5Z18KSLkEwV49++Mgof3HPL3l+dOH0sXj+sBB6xGXzCSCfcbT4WQcHEBPHuucNRus5LEsgJcls5r278igUL4yWoKH9oxrJ3r27cotea344LwzDZEjQzv40U0UPP4S9O+emtsVicFnXRqGS2QKmxNOw0XQ1mKYTb3jDGzr+7m1vextve9vbVraqTWAtxk+uFavdJXY6dQD8h688yMnREipSCDnvBNDkAOKKMAH05R1cx6Zc0w5gsa7YeDrZ2clqTzoBrWukS1qDQI+HtC1JPuckBvrWO39G1QsIIz33Iec6/PbbX7bodefr+gwW9OyEWj3Ezji89MKhxMDP/6zFf99PmCohwyZh+ggarNX4ybVgLRqBOlUSTRVrWAJ8QDUqZGIJZL0znpNBGMyn2DtSIFIwMVPDq4daAdOSePWwraFPOZLfePUF/M+/+1Wb364Pi2n4NCMFZNIOn77hlQAd399P3/DKZb/37cJ5A3mXUjXgG79/XcvtvfRZMxjAOIKEXtNaX+tGoNj4hJFqGH7VNEN3zoiKxlD6jGtTqtQ5OVoil7HJuhbZtKWrasIQ2aGD1vMjvvfgM/pabExoSCdjo47OQDZOOsMDmZYcSbfluN2wnHBer33WDIZNcwS91j+wGVrrG5GTuOO+p7j74eOUq37DIAJCIERrBZAUUMilSKdsipU6ow2j5Ach/XmXiRmPSi3QzoPFlUs9Xx811tMJNHcye405zZYlCOc1rtmWVuicnyhfSw4fGaVYrnNqvIJjCQYLLrYtO4bzjK6/odcwJ4IGG1290S4nceudP2OgkKbiBQumkK3EWdxx31Pcdf/TSdlLXAEUNql9Sinoz+lB68VKnZlS6660HiimZqvU/KgnJCLi04xSjfnAovGaInBTkl//l7t56sQUti1a/o5LOYGVOuXmv+OuwQxTsx5j0zX2juT56LtetqgsiKkUMvQKxhE02GiBrkMPHW0ZKCOFwA8iql7I3l05To2V+K/f+ifCUOE6FoN9bscEdicjdvfDx4mUwpaSENViyGMpaNuSFCs+U8XOYYmZ8tpPCrMt0bVjSdmSIFLsGswwU6pT8wIQIGns9lN6DKdtSfYM5zlxupjU/e8ZzvHGK/dw6KGj3Hbo8bZGfjWFAvPj/fmMQ60e0JdzFw09GTE4Qy9hHEETGynQ9fyZ2ZaBMvFQFj8IqXghM6U6YagagmUqkSewbcFf3PPLxPBnXIvZUp1cxm4xYj9/2Qh1P0QpWvT+LSkYyLtYlqBY8al6XocV8dQFhgAAHohJREFUrh/plNXVzGPBXCezY0vGpqp6GAtapTloxIaCQEtqV6o+d93/NAMFlwtG8nh+yGypzt8+8iz5rNPRyK8mebvSeL8RgzP0EsYRbBJBqFoGysRECs6Ml5P4umLOkI9OVOjLOcyWyzi2lo+IlEo6ZCdmPIIwQgB/8/et0+NsS9BfcJFCUKr4VL3uZSXWgjhxbFsCr9vB9wKUAEsI+nMpJmZqSeKh+SwRRHPjG8NIMTXrkXIscmm7oeOj2DmQAdob+dUkb02833AuYBzBBjE/fBMX48cDZZrpFDCJlGK6MfwkUloKwfO1Q5kpeUgpF0z7si3BQMFFCEGxXO9qJ74uCHSIqmk2byxF0akxLZ6k9t7fuJQfPnaS4YE0k0UPwoX3ayZSc2MegzBKnidmvpFfjTE38X7DuUBXWkOG1ZHU8M9WKWRsTo0VqdQC3bkb6tj9fGO1FDppKrAtXdcfRq0hIMeW7BxIM1hIU6z4jE1V19wJCCBlL71wKSBlW129RluK5H5SCm7+7ddw6b5BylWfsemaDgM1XvOiaxMwXfT0eM55nna+ke9G/6kTnWTATdjHsJUwJ4J1YP7uv1iuJzHoctVPRhoqdOULjfGSyyGu/dcJ17kHO7ZkoDEPeLZS7z4Ms0ykgME+l0LW5bkzs4sOtomUDt+gdInnYkliKQVCCRSKXMYB4M8OPU46JfHqjbLVkCW3MAI9VL4vl0qMe6cd+2qTtybeb9jqGEewhjRr+MT15FOzVU6NV9g1qGPU0yUPgSBl6yqhlC2p+1HXfiBla8nk+SGgeBqYAmbLPp6/fiEgS4rG4BmtO7TYeMY4MZxO2TiWZKDgcnq8hOdHCx5jW3rGgWNLsmmHPcOFJJGbz6ZJ2Rajk9WkA3qx5w0j3TvwqfdfCXTuIo4xxtywnTGOYI2Yk3DwGhOrYHymxvBABscSjE9VmSl5VL1Qa/oocFM25w/nUEpx7MXZRa+fciRhpJLqohjXkXogvFIrdgACvVPfvSNLEESMz9Q67tpj2eZco0xy11CWydkaqGjBHAIBOjzVGMLS/F796aF/ToTrHEvg2JKh/vSCXftthx5PErm5jMPIDsHZyYrWQ1rk9bTrIo5Pap3KSA2G7YpxBGtEs4SDjknPDYPJuBbTJR/VMOJK6X+qXsDzo0UKWadj4jTlSMJQzxJoxk1ZDORdgjDSMtNBh9FaXaDQoxZfOLv0VDDRuG9zHP0v7vklJ0eLesJXc6+CJdrG2q+6YoRv/P5bW26LjfT8Xfv8RG4ubZNJ25QqC3sbGr1mZDPOgjh9L4kKGgy9hnEEa8ToZAWJ1u2Pk796EIlW87QtoTt75+20637E1KxHfy5FseKDhChUpFIWvh8tcACZlEVf3sUPIqZma20dgC0FSrDguRaj26likYrzA5mWHfXX7vo5lVqAinTvAgLO35Xnt9/evrt2Pp1CM+2qcmpeQMPXEjW9fEsKdva3T9YaoTeDoTPGEawRGdfihdESQgKh3tlrO6wdw2AhRa0eIURrbDwOtfQXXKQU1LyQahQsSPJmXFvLJwcRk7O1BTkC0InnKKIrJc7kMaI7JxDX3Sh0Qro51HPVFSO87fX7ufvh4/hhRDZt8+43XsSNb7287bWWQ7tE7umJClIKLCkJiQgb8wTCqLOUxHoKvW3UHAuDYb0wjmCNEA1TKYVACbXAuE4V6033nRsAk7IlSsFsqU6lFiyI8XfjAGIsKZY1MB66PwnE67UEC9Zw+MgoDz52ksFCit1DGTw/5MHHTnLpvsGuDWJsTJuHwu/bXUiMavN1Dv5f309iaJYlsdDjVBGi4/OtV+OXCTkZzgVMH8EaUfEChgcziC522PGvUw050HoQMVX08PwQN2XxysuG2bszx+4dWdIpi4mZ2pJOQAqQQuI6ct1mBVt6cg0Zt3X/sNoZy7ExPTVWpFz1qdUDipU6p8ZK/Nmhxzl8ZLTl/nuGcwShLg/1/JC6HxKEij3DnaeIraZXYDFW+9oNhl7AOII1YmQoi23pcAW0zrmdb5hTjsRNWXihSkJAAsi6Nr/zjn9BxnUo5B0mZ6pMFbVsxJIovcNdrJpmtQSR7oZ+9xsvarl9dLJCECpeHCs3BN/KBKHqOuwSG9NKLUAgsKVESv1zO6N6zYE9unS08XN8WrnmwJ6Oz7FejV+jkxVcx2q5zcwWMGw1tnVoaC1ju3FSs94I7bQzxn1ZG4Rgdp6aZz5jc/7OHK5r8/Onx3juTJGsa9GN/Y+Jn89fQ6no+YNlhID3X/fSBbH/rGtzcrTYiNtDEEaMTVXZN1Lo6nni+H2swgpxoj1qa1SfODbBYF+aSk0rt+q+A5snjk1w4yLPsx69AkZryHAusG1PBPNlH+LY7vwwRLfEO860a+sBME2/c1MWrmMxWwlanICeBuZQyKaYKftMzNR46sQUs+U6J04XUW10iOYj0EnitT4FSAG2LbEtyXk7c+zdlePlF+9smwBOziCNsti5H7tb1chQFs8PcWxJqBT1IMTzI6JIayvNN6qjkxUG8inOH86x/7wC5w/nGMinNmUXvl4hJ4NhI9m2J4K1LCdsPlnsHEgzW5JIofDqEfUwatH4kSJ2DJK061DzQt1Y1RChk1LQn3epeYFWnugi36BW3kLQgutIXfqKDgM5QjDY7+pd/iLGreqFDA+kmSnXkx2661icGivz0S/dv+RpKz5NWZYk9JqS5UIxXfS4/uoLW+7fS7twM1vAcC5wzjuCTuGftSonnF81Uvf1PN+KF1L1Wh3A7h05HFs3ms1W6pydqiZVPnEYRioYLLikHIvpopcMYVnKITQjGv9a7mOihhaQFIKgHuKHEaOTFbJph3e/8aIlK3LOH84DUK76nJ2qYluybSVNu7/Jxw8e4Ct/9dNEUlsIQcppH/LpNcVPI09h2Oqc06GhxcI/cTiimZXsKuOTRca1CcKIiRmPiVkvcQJ2Qz6hkHPoyztMF+v/f3v3Hhxlee8B/Pvuu+9ecueSBAmQgEhAPAwi9RjpkXIaoSHc6wVKB61MBeoUSy2tUAaHOQWkxYFSatpxPGU6ZY5F4dTjSJQSi3oSShuOliIYIFwSzBUScs9e3n3OH+/uurnsJhv2wrv7/cw4Y9a9PJs47+95f8/z/H6ob+5ES7u9z1ZPg7uP8BeN7Whs7gQgkDncipREEywmuZ9P79vPAMCg7iT6e42nGqrd6V7AlrT3t5gM+ODv1X7TZr3TI02t3QCA4amWPjtp/P1NAK2ExISsFEzISsX40SnISk9CWpK5T3BmxU+i0IrpO4JA6Z9gZ5X+7ixu3OqEUZZQXd/ep8zz8BQzhNDKInR0OXCx6hZUlzbrlnxWYgUAi1mGw6HCKbQ0DKClXLpsXZAkYHiKBcNSLFo5CYfqbR4fqIl8oKJsgHYXoCha0TvtYJt2V+BStX83GgxwuQQ6u50YkWr0mzbrnR5xCSB9mBWJli//9/LcbQX6mwST8vE3C+fhLqLgxXQgCJT+CSa329+hof98+58oP1+HphZbjzsLq1mGw6lieIoVVrMRrR023Gzpht2pencBqaq2COyb2k+wGNHidMEgCbggepSHEAK42dINk2JAklVxnycIPOWXDVpJC6envpH7ce9hNgCZI7Qtrx1dTqQkmVBd364dcHOp3nr/UoDdO758L8ybi0rR3NrV4797LuiB/iZrl027rZQPD3cRDU1MB4KBZpiDze36zmJl2YBuu4q6pk5Ul171PsdikjE82YTkRBM6upyQDBJqbnRo6R5V9NkK6pnISwAkAzA6PRmq2gaLWUZDk3YR7T2jtztcaHL032PY81zPhV51aWUX0pJNMClGrPUJehazDAkSumxaWmX1Ii0Aei7gnpaXBvd7KkYDbA4VFrOMzUWlA862A91tHTlxye/f5HYXXllPiGhoYjoQhGpRsb6pE8OSFLR1OlHf1NkjHTNr2mhMyErB+avN6Ox2INGq4IEpafjg79UwSBJUl8v7/N778gFtl5Bnb77nQmwwSFAMUlAVRYVPmslDlrUzC2MzLYMKep7fV4LFiFttNrgkbQdRgkVBe6cDkiRBVV0DzrYHuqAH+pvczsJrOOsJEcWymA4Eodja19zaDcUo4WJ1S49ZvNUsY3RGEp5ZOBXJiQoe/7pWYsDjnrHDvE1qtCb17ruA3jX7JaDsTC1WzJ385TZKgxZAPBd3o0EKqpCcV5D7+X1/X6oq4FBdMMoSRqcno63DDqeqDnq27e+CHs7tlnfStlIiPYnpQAAMfYZZf7MDh0ou4IPy694SD5IEJFkVJFoVJFkVrJw32e9FxvO55efrsfvgaW0bqPuAmO813akKXKtrRfn5eu9F0hNAJPfBNMm9shxsQDAaJaQmmtFtUwe9iOrv97V6+59DNtsO13bLO21bKZFeRDwQnD59Gjt37oTD4UBaWhp27NiBrKysSA/Dr6q6Nhw6XoH//UeNN6WTaFXwL3ePQHNrN1SXwKiRiZj7YDbuz80Y1HuOTLPgWm0bgP538QgBHHj3M+8F0hNAPAHBYABMknbqNhDfhWBPMsqhCiQnKD0WUWsa27HjwN+QYDFi3KiUQc3I9TDb5uEuoqGJeCDYuHEjXn31VUyePBlvvfUWfvazn6GoqCjSw+jjYnUz/vjnC/jbZ3XeREpqkhmPPjgO/zY9CykJJiQmGGE1K31e62+27dnF4lBVrXuXn5S/QQJqGnt2B/MNCJ7yzJ3dTpjMErpsfd9oWLIZEALN7Xbv+O1OFfY2FQaDNntXXUB1Q7u32U1Xt3PQO2v0Mtvm4S6i4EU0ENjtdjz//POYPFmrV5Obm4s//OEPkRxCH2crb+LQ8Qp8cqHR+9jIVAvm5eXgofvuQpJFQVKCAou5/19VoC2Lnl0sLR1OGCQDjIrU5xCbsXdhol58L2yeoFBZfQvdDhVCCJgUGUb3VtHm1i93FBncvQ60hV87RqRacONWN1Sfll52pwuqKryHvQJdQDnbJopdEQ0EJpMJixcvBqA1Etm/fz/y8/MjOQQAgBACpz9vwKHjF3D+apP38btGJuIbeTn4ypQMWM3aOoC/AOBx5MQlOFQVLR09K2F67hB6V9XsU9HT3UxmTEbSgOP2N9v9r2Of462Si961B1mWIEFCWrIFCWYZrR0ONLfavtxi6rPV9Fa7DaNHJg4q18/ZNlFsClsgKC4uxs6dO3s8NmHCBBw4cAB2ux0vvvginE4n1qxZE64h9OFyCZSeqcFbJRdxuabF+/i4UckoyMvB9EnpsJiMSLYqMA8QADyq6lrR0eWABHeNHqdAS7sdTrUV40aloLm1C4rRAKfT3cdY7tm32CBJSEpU8FThvT3eN5gTsv+svImM4VbcbOmG0ylgMEhwCa1gm2wwIys9CVX1bZDddx6epQaDQTssdqfl+okossIWCAoKClBQUNDn8Y6ODqxbtw5paWkoKiqCovTNuYea6hL4S3kVDv/lEq43tHsfv2dsGr6Rl4Op44fDHGQA8HCqAi4hINx9c91NvOBURY99+S3tdm9jl5REBV021e9ibbAnZD13HmlJZjTe6oLWtVHr4OV0CqxedC8OvPsZ6m50QBZaQJQMWhDSTh/febl+IoqcqCwWZ2dnY9u2bTAYwlvzzu5QcezUNfz3iUtoaP6y5MHUCSNQkJeDe8amwWwyItFq7LEbBghmRi7gSbtL0Gbbnhm3b17dqfbfi7c/wZ6Q9ezoSbRqQfVWuw12h4DFbOxRjG3fHz/RmrkIFcIFOCWBcRlJeLpwKlM+RHEsooHg3LlzKCkpwcSJE7F06VIAQEZGBl577bWQfk6XzYl3Sy/jnY8vo8m9gCoBmJ6bjoK8HGSPSoHFZERCPwEACHZGLmmNYQR63BF4DCWvHuwJWd8dPQkWI2RZm+X3rshpszthd6recVpNMoMAEUU2ENx7772oqKgI2/u3ddjwPx9dxrtlV9DWqXUC0w6BmWBwN1cRQmBEmrVPn1ngy7uAz682wSBJPcoo+5uRG931+yVJ8i7CuoRAl80xqKYs/Ql2z/5gdvQcePcz2BwuGA0G7zhtDpf3/ELv3wGrdxLFj5g4WXyzpQt/+rASx05dQ2e3E4B2gc7NHo6axjakJJowPMUCh1PFwfc+h0kx9rm4+d4FCCGgCoHG5i7AXU7Z34x83KgU1DS2efvnGgwSXA4Bk9J/U5bBGMqe/YHuPGoatQJ4Bnf/Asndicb3/AKrdxLFJ103pqm72YFX3/oH1uwswZ8+rNQOXCkGfP0rY/Efax+GbJCQlZ6EkalWdNtVdNtdcAmBIycu9Xkv37y8YjRAgjbDv9WmpZb8zciXfW0iFFnGiFQLskcla7uHDBKGp/RtyjJY4Wq80vtccu+ffX8HQx07EemPLu8IrtW24vBfLuHjT6/D6d6KaTUbMeeBMfj3mWORnGhCgllBt90Jg4QeDWP8zex98/Ke3TeS0BacAzUk79uURSA9zeJduA30mYGEes9+VnoSquvb4PKpdyQEepxfYPVOoviku0Dw6uF/4LNqFS731pzkBAVf/8o4zJ4xBgkWIxLMWlE4xWiA2WQcdK7dNy/vuYg3tXYDAhiWYg2YKx9sUxZ/IpGXf6rwXu+uIafqglE29Dm/oId6QkQUerpLDf3f5w1wCYFhyWY8kT8J29fNwvyHc5CeakV6WgLSks1QjNrX6t1LN9DM3ve57Z12NLV2w6EKZKUnBXVhDuYzgcB9lUNp5pRMrH/yfkzKHoaRaVZMyh6G9U/e3+N7BTt2IooNursjGJ5qwZL8KfjX+0a5a+lodwBGuW9MC6Y+jue5B979DLU3OmGUDcgYZoFTVYNaMA22Js/tdNUK9k5ioHQT6wkRxSfdBYINy+/HmDGjkWhRkOAnAPgKJtc+c0omjpy4hLtGunqkR4JtdxjMZw41Lz+UHT6DCRysJ0QUf3QXCJITzMgYlgB5gAAwVKFYMA1mpj7UvHywdxLcGkpE/uhujSApQQlbEAC0C3PvUtHBLJgGm/Mfal6+vqmzz6G4QAGLW0OJyB/dBYJwu90F02AvuEM9MxBswAo2cBBR/NBdaijcbnfBdCippWDz8uXn69HWYUfNjU4osoRhyWYY3aWu/QUsbg0lIn8YCPpxOwum4b7g+ub6M4ZZ0dxqQ+OtbozJTMLqRf4LyOml1SQRRR5TQyEW7r34vqmnJKuCsZlJuGtkAlISzQNuDQ1H2Qoi0j/eEYRYuPfi386uJm4NJaL+MBCEQTgvuMz1E1GoMTWkMywDQUShxkCgM8z1E1GoMTWkQ8z1E1Eo8Y6AiCjOxfwdAXvwEhEFFtOBQC+F1hisiCiaYjo1pIdCa5FqTENE5E9MBwI9FFrTQ7AiotgW04HgdktKR4IeghURxbaYDgR6OHylh2BFRLEtpgOBHg5f6SFYEVFsi/iuofLycuzYsQMOhwNZWVnYtWsXUlNTw/Z5d/rhKzaMJ6Joi3gg2LRpE4qKijBx4kTs3r0br7/+On74wx9Gehh3lDs9WBFRbIt4IDh69CgURYHD4UB9fT1yc3MjPQQiIvIR8TUCRVFQUVGB2bNn49SpUygsLIz0EIiIyEfY7giKi4uxc+fOHo9NmDABBw4cQG5uLsrKyvDGG29gw4YNeOONN8I1DCIiGkDYAkFBQQEKCgp6PGaz2XD8+HHk5+cDABYtWoRdu3aFawhERDQIEU0NGY1GbNu2DWfPngWg3TXMmDEjkkMgIqJeIrpYLMsy9uzZg61bt0JVVWRmZmL79u2RHAIREfUS8V1DM2fOxJEjRyL9sURE5EdMnywmIqKBMRAQEcW5mG5ME01sNkNEesE7gjBgsxki0hMGgjBgsxki0hMGgjBgsxki0hMGgjBgsxki0hMGgjBgsxki0hMGgjDQQ2c0IiIPbh8NEzabISK94B0BEVGcYyAgIopzDARERHGOgYCIKM7pZrFYVbV9+XV1dVEeCRGRfniumZ5raH90EwgaGxsBACtXrozySIiI9KexsRHZ2dn9/jdJCCEiPJ4h6e7uxtmzZ5Geng5Zlgd+ARERQVVVNDY24r777oPFYun3OboJBEREFB5cLCYiinMMBEREcY6BgIgozjEQEBHFOQYCIqI4x0BARBTnGAiIiOKcrgNBeXk5li1bhoULF2Lt2rVoaWmJ9pBC5vTp03jsscewePFiPPXUU/jiiy+iPaSQ2rt3L371q19Fexgh8c4772D+/PmYO3cuDh48GO3hhFx7ezsWLFiA69evR3soIbV//34UFhaisLAQP//5z6M9nJD65S9/ifnz56OwsBC/+93vBn6B0LH8/Hxx8eJFIYQQv/jFL8Qrr7wS5RGFzpw5c8T58+eFEEK8+eabYu3atVEeUWi0traKTZs2iWnTpol9+/ZFezi3ra6uTsyZM0c0NzeLjo4OsXDhQu//k7Hg008/FQsWLBBTp04V1dXV0R5OyJSWloonn3xS2Gw2YbfbxapVq8SxY8eiPayQOHXqlFi+fLlwOByiq6tLzJkzR1RWVgZ8ja7vCI4ePYqJEyfC4XCgvr4eKSkp0R5SSNjtdjz//POYPHkyACA3Nxe1tbVRHlVolJSUICcnB9/5zneiPZSQKCsrw0MPPYS0tDQkJCRg3rx5eO+996I9rJA5dOgQXnrpJWRkZER7KCGVnp6OF198ESaTCYqi4O6770ZNTU20hxUSDz74IH7/+9/DaDTi5s2bUFUVCQkJAV+j60CgKAoqKiowe/ZsnDp1CoWFhdEeUkiYTCYsXrwYAOByubB//37k5+dHeVShsWTJEjz77LMxUy+qoaEB6enp3p8zMjJQX18fxRGF1vbt2zFz5sxoDyPk7rnnHkyfPh0AcPXqVRQXF2P27NlRHlXoKIqCffv2obCwEHl5ecjMDNw2VxeBoLi4GI888kiPf55++mkA2my5rKwM3/ve97Bhw4boDnQIAn03u92OH/3oR3A6nVizZk10BxqkQN8rlrhcLkiS5P1ZCNHjZ7qzXbx4Ec888wx+/OMfIycnJ9rDCan169fj5MmTqK2txaFDhwI+VxdlqAsKClBQUNDjMZvNhuPHj3tnyosWLcKuXbuiMbzb0t93A4COjg6sW7cOaWlpKCoqgqIoURjd0Pn7XrFm1KhRKC8v9/7c2NgYc2mUWHX69GmsX78emzdvjplsAgBUVlbCbrdjypQpsFqtmDt3LioqKgK+Rhd3BP0xGo3Ytm0bzp49C0Cbgc6YMSPKowqdjRs3Ijs7G3v37oXJZIr2cMiPhx9+GCdPnkRTUxO6urpw7NgxPPLII9EeFg2gtrYWzz33HHbv3h1TQQAArl+/ji1btsBut8Nut6OkpAQPPPBAwNfo4o6gP7IsY8+ePdi6dStUVUVmZia2b98e7WGFxLlz51BSUoKJEydi6dKlALTc82uvvRblkVFvmZmZ2LBhA1atWgWHw4HHHnsM06ZNi/awaACvv/46bDYbXn75Ze9jy5cvx4oVK6I4qtCYPXs2zpw5gyVLlkCWZcydO3fAYMd+BEREcU63qSEiIgoNBgIiojjHQEBEFOcYCIiI4hwDARFRnGMgIBqiM2fOYOvWrUN+fXV1Nb7//e+HcEREQ8NAQDREly5duq26QjU1Nbhy5UoIR0Q0NAwERG5btmzBnj17vD+//fbbeO655/p9bm1tLfbt24fy8nJs2rQJAPDBBx/g8ccfx5IlS7B8+XJ88sknALQj/8uXL8eyZcuwdOlSHDx4EKqqYsuWLaiqqsLq1avD/+WIAolEfWwiPTh37pyYNWuWcDgcQgghvvWtb4mPPvrI7/MPHz4snn32WSGEEFeuXBELFiwQTU1NQgghLly4IGbNmiU6OjrEpk2bxG9/+1shhBANDQ3iBz/4gVBVVfz1r38VhYWFYf5WRAPTbYkJolCbMmUKxowZgxMnTmD8+PFoaGjAV7/61UG9trS0FA0NDT0qrEqShKqqKjz66KP4yU9+gjNnziAvLw9btmyBwcCbcbpzMBAQ+Vi5ciUOHz6MnJwcPPHEE4MuKe1yuZCXl4e9e/d6H6utrUVGRgYmT56M999/H2VlZTh58iR+/etf48iRI+H6CkRB47SEyMe8efNw/vx5vP/++/jmN78Z8LmyLMPpdAIA8vLyUFpaisrKSgDAhx9+iEWLFqG7uxsvvPACjh49isLCQrz00ktISkpCVVUVZFmGw+EI+3ciGgiLzhH1snPnTty4cQOvvPJKwOddu3YN3/3udzFp0iTs378fxcXF+M1vfgMhBIxGIzZv3oyZM2eisrISP/3pT9HZ2QlZlpGXl4eNGzeipaUFq1atgslkwptvvsmGNhQ1DAREPjo7O/Htb38bW7du9bYyJIp1XCMgcvv444/xwgsvYMWKFZg+fTouX77st/3p+PHje6wHEOkZ7wiIiOIcF4uJiOIcAwERUZxjICAiinMMBEREcY6BgIgozjEQEBHFuf8HfHJkharkOSoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#使用散点图观测（10分）\n",
    "import seaborn as sns\n",
    "sns.regplot(x=y_test,y=y_pred)\n",
    "plt.xlabel('y_test')\n",
    "plt.ylabel('y_pred')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:25.593168Z",
     "start_time": "2020-06-16T00:15:25.588171Z"
    }
   },
   "source": [
    "'''\n",
    "图中我们可以看到，如果完全拟合，散点应该和直线相重合，这里发现，y_test有一些的异常值，\n",
    "而线性回归模型的一大缺点就是对异常值很敏感，会极大影响模型的准确性，因此，下一步，我们就根据这一点，对模型进行优化\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制残差图（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8e4d4908>"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'y_test')"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y_pred')"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制残差图查看模型的拟合情况（10分）\n",
    "from sklearn.model_selection import cross_val_predict,cross_val_score\n",
    "sns.residplot(y_test, y_pred,lowess=True,color=\"b\")\n",
    "plt.xlabel('y_test')\n",
    "plt.ylabel('y_pred')\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制相关性热图（10分）\n",
    "\n",
    "**绘制各预测变量间的相关性热图，查看是否存在相关性很高的变量（共线性或近似共线性）**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8e921588>"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "sns.set(style=\"white\")\n",
    "corr = Scaled_Data.corr()\n",
    "f, ax = plt.subplots(figsize=(15, 12))\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "sns.heatmap(corr, cmap=cmap, vmax=.3,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .2},annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型调优"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 删除异常值（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:34.870734Z",
     "start_time": "2020-06-16T01:35:34.674966Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8e5449b0>"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#####================寻找异常值====================######\n",
    "Scaled_Data['Next_Tmax'].plot.box() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.778314602481859"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#异常值为小于 Q1-1.5IQR 或 Q3+1.5IQR\n",
    "Q1 = np.percentile(Scaled_Data['Next_Tmax'], 25)     \n",
    "Q3 = np.percentile(Scaled_Data['Next_Tmax'],75)    \n",
    "IQR = Q3 - Q1   \n",
    "outlier_step = 1.5 * IQR\n",
    "low_whisker=Q1-outlier_step\n",
    "low_whisker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2650</th>\n",
       "      <td>-2.086416</td>\n",
       "      <td>1.146897</td>\n",
       "      <td>-2.217884</td>\n",
       "      <td>-1.247235</td>\n",
       "      <td>-0.404519</td>\n",
       "      <td>1.025171</td>\n",
       "      <td>1.294520</td>\n",
       "      <td>0.671912</td>\n",
       "      <td>0.915515</td>\n",
       "      <td>-0.086042</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>-0.295389</td>\n",
       "      <td>-2.938450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2750</th>\n",
       "      <td>-2.086416</td>\n",
       "      <td>0.722336</td>\n",
       "      <td>-2.347124</td>\n",
       "      <td>0.720893</td>\n",
       "      <td>0.431804</td>\n",
       "      <td>0.990499</td>\n",
       "      <td>1.166849</td>\n",
       "      <td>1.394751</td>\n",
       "      <td>2.292510</td>\n",
       "      <td>-0.188155</td>\n",
       "      <td>3.329386</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>-0.592600</td>\n",
       "      <td>-3.002504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5025</th>\n",
       "      <td>-1.443736</td>\n",
       "      <td>1.297482</td>\n",
       "      <td>-2.698896</td>\n",
       "      <td>3.154805</td>\n",
       "      <td>0.682660</td>\n",
       "      <td>1.256502</td>\n",
       "      <td>1.666250</td>\n",
       "      <td>1.293021</td>\n",
       "      <td>0.393944</td>\n",
       "      <td>-0.067187</td>\n",
       "      <td>0.123279</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.165416</td>\n",
       "      <td>-3.066558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5026</th>\n",
       "      <td>-0.564278</td>\n",
       "      <td>1.141629</td>\n",
       "      <td>-2.189783</td>\n",
       "      <td>0.265648</td>\n",
       "      <td>-0.153150</td>\n",
       "      <td>1.590055</td>\n",
       "      <td>1.613945</td>\n",
       "      <td>1.371150</td>\n",
       "      <td>0.589398</td>\n",
       "      <td>-0.085760</td>\n",
       "      <td>0.215901</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>0.865895</td>\n",
       "      <td>-2.810342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5050</th>\n",
       "      <td>-3.067350</td>\n",
       "      <td>1.585246</td>\n",
       "      <td>-2.476358</td>\n",
       "      <td>1.396136</td>\n",
       "      <td>-0.782847</td>\n",
       "      <td>0.501491</td>\n",
       "      <td>-0.052399</td>\n",
       "      <td>-0.043444</td>\n",
       "      <td>0.101308</td>\n",
       "      <td>0.072443</td>\n",
       "      <td>-0.138037</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.131952</td>\n",
       "      <td>-3.066558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6100</th>\n",
       "      <td>-1.646687</td>\n",
       "      <td>0.457765</td>\n",
       "      <td>-2.310616</td>\n",
       "      <td>1.833989</td>\n",
       "      <td>1.489480</td>\n",
       "      <td>1.192236</td>\n",
       "      <td>0.211053</td>\n",
       "      <td>0.341799</td>\n",
       "      <td>1.351480</td>\n",
       "      <td>-0.204227</td>\n",
       "      <td>0.056343</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>-1.511327</td>\n",
       "      <td>-3.034531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6175</th>\n",
       "      <td>-2.695272</td>\n",
       "      <td>1.457979</td>\n",
       "      <td>-4.087857</td>\n",
       "      <td>4.911091</td>\n",
       "      <td>-0.210420</td>\n",
       "      <td>1.489658</td>\n",
       "      <td>2.281600</td>\n",
       "      <td>2.656311</td>\n",
       "      <td>2.268567</td>\n",
       "      <td>-0.017489</td>\n",
       "      <td>1.340116</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>-1.786106</td>\n",
       "      <td>-4.123453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6176</th>\n",
       "      <td>-2.086416</td>\n",
       "      <td>0.517310</td>\n",
       "      <td>-3.535766</td>\n",
       "      <td>4.003889</td>\n",
       "      <td>1.463660</td>\n",
       "      <td>0.894764</td>\n",
       "      <td>1.277177</td>\n",
       "      <td>2.148085</td>\n",
       "      <td>1.600057</td>\n",
       "      <td>0.084708</td>\n",
       "      <td>2.187907</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>-2.138584</td>\n",
       "      <td>-3.258721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6177</th>\n",
       "      <td>-1.815814</td>\n",
       "      <td>0.293287</td>\n",
       "      <td>-3.323528</td>\n",
       "      <td>3.380546</td>\n",
       "      <td>0.732305</td>\n",
       "      <td>1.483952</td>\n",
       "      <td>1.747604</td>\n",
       "      <td>2.166054</td>\n",
       "      <td>1.581048</td>\n",
       "      <td>-0.091321</td>\n",
       "      <td>1.378723</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>-2.150110</td>\n",
       "      <td>-3.194667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6178</th>\n",
       "      <td>-1.815814</td>\n",
       "      <td>1.381461</td>\n",
       "      <td>-3.854778</td>\n",
       "      <td>5.508391</td>\n",
       "      <td>1.053051</td>\n",
       "      <td>1.129946</td>\n",
       "      <td>2.138966</td>\n",
       "      <td>2.557734</td>\n",
       "      <td>1.943433</td>\n",
       "      <td>0.196184</td>\n",
       "      <td>1.718829</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>-2.174068</td>\n",
       "      <td>-3.098586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6179</th>\n",
       "      <td>-1.748163</td>\n",
       "      <td>0.540625</td>\n",
       "      <td>-3.180434</td>\n",
       "      <td>2.709573</td>\n",
       "      <td>0.667897</td>\n",
       "      <td>1.427806</td>\n",
       "      <td>2.207221</td>\n",
       "      <td>2.466783</td>\n",
       "      <td>1.705215</td>\n",
       "      <td>-0.208367</td>\n",
       "      <td>1.190854</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>-2.181193</td>\n",
       "      <td>-3.194667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6180</th>\n",
       "      <td>-1.849639</td>\n",
       "      <td>0.291101</td>\n",
       "      <td>-3.008869</td>\n",
       "      <td>3.443116</td>\n",
       "      <td>0.875766</td>\n",
       "      <td>1.527562</td>\n",
       "      <td>2.186085</td>\n",
       "      <td>2.380435</td>\n",
       "      <td>1.732806</td>\n",
       "      <td>-0.111499</td>\n",
       "      <td>0.530213</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>0.637074</td>\n",
       "      <td>-0.133199</td>\n",
       "      <td>-0.810993</td>\n",
       "      <td>-2.150112</td>\n",
       "      <td>-2.906423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181</th>\n",
       "      <td>-2.086416</td>\n",
       "      <td>0.726924</td>\n",
       "      <td>-3.416935</td>\n",
       "      <td>3.153251</td>\n",
       "      <td>1.033799</td>\n",
       "      <td>1.484045</td>\n",
       "      <td>2.193447</td>\n",
       "      <td>2.587556</td>\n",
       "      <td>2.013460</td>\n",
       "      <td>-0.123314</td>\n",
       "      <td>0.618552</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>-1.931225</td>\n",
       "      <td>-0.911963</td>\n",
       "      <td>-0.845437</td>\n",
       "      <td>-2.193176</td>\n",
       "      <td>-3.034531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6182</th>\n",
       "      <td>-1.849639</td>\n",
       "      <td>0.128079</td>\n",
       "      <td>-2.987955</td>\n",
       "      <td>3.182614</td>\n",
       "      <td>0.539878</td>\n",
       "      <td>1.737777</td>\n",
       "      <td>2.324832</td>\n",
       "      <td>2.536665</td>\n",
       "      <td>1.386484</td>\n",
       "      <td>0.099575</td>\n",
       "      <td>0.452257</td>\n",
       "      <td>-1.490051</td>\n",
       "      <td>-1.024766</td>\n",
       "      <td>-0.172266</td>\n",
       "      <td>0.223191</td>\n",
       "      <td>-2.197299</td>\n",
       "      <td>-2.970477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6183</th>\n",
       "      <td>-1.883465</td>\n",
       "      <td>0.559954</td>\n",
       "      <td>-3.090460</td>\n",
       "      <td>3.785644</td>\n",
       "      <td>0.588896</td>\n",
       "      <td>1.794153</td>\n",
       "      <td>2.241939</td>\n",
       "      <td>2.621514</td>\n",
       "      <td>1.616357</td>\n",
       "      <td>0.058270</td>\n",
       "      <td>0.359513</td>\n",
       "      <td>-0.953787</td>\n",
       "      <td>-2.082302</td>\n",
       "      <td>-0.201502</td>\n",
       "      <td>-0.616299</td>\n",
       "      <td>-2.121602</td>\n",
       "      <td>-3.066558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6184</th>\n",
       "      <td>-2.390844</td>\n",
       "      <td>0.548491</td>\n",
       "      <td>-3.356360</td>\n",
       "      <td>3.778144</td>\n",
       "      <td>0.340973</td>\n",
       "      <td>1.722154</td>\n",
       "      <td>2.379023</td>\n",
       "      <td>2.612524</td>\n",
       "      <td>1.583916</td>\n",
       "      <td>0.143382</td>\n",
       "      <td>0.585596</td>\n",
       "      <td>-1.758184</td>\n",
       "      <td>-0.458230</td>\n",
       "      <td>2.701715</td>\n",
       "      <td>2.861413</td>\n",
       "      <td>-2.358212</td>\n",
       "      <td>-3.194667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6185</th>\n",
       "      <td>-1.748163</td>\n",
       "      <td>0.073370</td>\n",
       "      <td>-2.977437</td>\n",
       "      <td>2.994312</td>\n",
       "      <td>0.760935</td>\n",
       "      <td>1.561845</td>\n",
       "      <td>2.072250</td>\n",
       "      <td>2.185478</td>\n",
       "      <td>1.552148</td>\n",
       "      <td>-0.177150</td>\n",
       "      <td>0.826247</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>1.178432</td>\n",
       "      <td>-0.611095</td>\n",
       "      <td>-0.462470</td>\n",
       "      <td>-2.131853</td>\n",
       "      <td>-3.066558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6186</th>\n",
       "      <td>-2.357019</td>\n",
       "      <td>0.397078</td>\n",
       "      <td>-3.309947</td>\n",
       "      <td>3.641401</td>\n",
       "      <td>0.469220</td>\n",
       "      <td>1.452282</td>\n",
       "      <td>2.096269</td>\n",
       "      <td>2.496450</td>\n",
       "      <td>1.974161</td>\n",
       "      <td>-0.132036</td>\n",
       "      <td>0.559798</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>-0.042770</td>\n",
       "      <td>1.294304</td>\n",
       "      <td>-0.484508</td>\n",
       "      <td>-2.046890</td>\n",
       "      <td>-3.739128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6187</th>\n",
       "      <td>-1.815814</td>\n",
       "      <td>0.203483</td>\n",
       "      <td>-3.346815</td>\n",
       "      <td>2.572588</td>\n",
       "      <td>0.430980</td>\n",
       "      <td>1.597681</td>\n",
       "      <td>1.913452</td>\n",
       "      <td>2.183086</td>\n",
       "      <td>1.684987</td>\n",
       "      <td>-0.163895</td>\n",
       "      <td>1.556727</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>1.153252</td>\n",
       "      <td>-0.037504</td>\n",
       "      <td>1.043125</td>\n",
       "      <td>-2.208229</td>\n",
       "      <td>-3.130613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6188</th>\n",
       "      <td>-1.917290</td>\n",
       "      <td>0.196464</td>\n",
       "      <td>-2.927473</td>\n",
       "      <td>3.375313</td>\n",
       "      <td>0.684771</td>\n",
       "      <td>1.560831</td>\n",
       "      <td>2.231691</td>\n",
       "      <td>2.590861</td>\n",
       "      <td>1.712657</td>\n",
       "      <td>-0.069786</td>\n",
       "      <td>0.474994</td>\n",
       "      <td>-0.953787</td>\n",
       "      <td>-0.810742</td>\n",
       "      <td>-0.569309</td>\n",
       "      <td>-0.466338</td>\n",
       "      <td>-2.181012</td>\n",
       "      <td>-2.938450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6189</th>\n",
       "      <td>-1.748163</td>\n",
       "      <td>0.335249</td>\n",
       "      <td>-3.121586</td>\n",
       "      <td>3.246297</td>\n",
       "      <td>0.445623</td>\n",
       "      <td>1.492428</td>\n",
       "      <td>2.204697</td>\n",
       "      <td>2.533502</td>\n",
       "      <td>1.996110</td>\n",
       "      <td>-0.150345</td>\n",
       "      <td>0.469066</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>-0.684844</td>\n",
       "      <td>-0.586289</td>\n",
       "      <td>-0.293244</td>\n",
       "      <td>-2.122811</td>\n",
       "      <td>-3.162640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6190</th>\n",
       "      <td>-2.052591</td>\n",
       "      <td>0.342238</td>\n",
       "      <td>-3.133392</td>\n",
       "      <td>3.608837</td>\n",
       "      <td>0.689004</td>\n",
       "      <td>1.649470</td>\n",
       "      <td>2.317912</td>\n",
       "      <td>2.603041</td>\n",
       "      <td>1.430013</td>\n",
       "      <td>-0.014496</td>\n",
       "      <td>0.546835</td>\n",
       "      <td>-1.490051</td>\n",
       "      <td>0.045358</td>\n",
       "      <td>0.376283</td>\n",
       "      <td>0.730359</td>\n",
       "      <td>-2.274529</td>\n",
       "      <td>-3.066558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6191</th>\n",
       "      <td>-2.018766</td>\n",
       "      <td>0.443371</td>\n",
       "      <td>-3.559795</td>\n",
       "      <td>2.056200</td>\n",
       "      <td>0.187604</td>\n",
       "      <td>1.580071</td>\n",
       "      <td>2.165352</td>\n",
       "      <td>2.341953</td>\n",
       "      <td>2.004631</td>\n",
       "      <td>-0.191814</td>\n",
       "      <td>2.098272</td>\n",
       "      <td>1.457418</td>\n",
       "      <td>1.354688</td>\n",
       "      <td>-0.154704</td>\n",
       "      <td>-0.408689</td>\n",
       "      <td>-2.146457</td>\n",
       "      <td>-3.386829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6193</th>\n",
       "      <td>-1.781989</td>\n",
       "      <td>0.683279</td>\n",
       "      <td>-3.546341</td>\n",
       "      <td>3.042051</td>\n",
       "      <td>0.351990</td>\n",
       "      <td>1.475464</td>\n",
       "      <td>2.204002</td>\n",
       "      <td>2.544886</td>\n",
       "      <td>2.064549</td>\n",
       "      <td>-0.164838</td>\n",
       "      <td>0.821398</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>-0.672255</td>\n",
       "      <td>0.243650</td>\n",
       "      <td>0.372715</td>\n",
       "      <td>-2.064552</td>\n",
       "      <td>-3.643046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6194</th>\n",
       "      <td>-1.917290</td>\n",
       "      <td>1.544363</td>\n",
       "      <td>-4.016045</td>\n",
       "      <td>5.989302</td>\n",
       "      <td>-0.963308</td>\n",
       "      <td>1.394206</td>\n",
       "      <td>2.330022</td>\n",
       "      <td>2.670813</td>\n",
       "      <td>2.371793</td>\n",
       "      <td>0.122695</td>\n",
       "      <td>1.598140</td>\n",
       "      <td>1.457418</td>\n",
       "      <td>0.158666</td>\n",
       "      <td>1.560277</td>\n",
       "      <td>2.534052</td>\n",
       "      <td>-1.925129</td>\n",
       "      <td>-3.450884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6195</th>\n",
       "      <td>-1.984940</td>\n",
       "      <td>0.181415</td>\n",
       "      <td>-3.033073</td>\n",
       "      <td>3.594376</td>\n",
       "      <td>0.729578</td>\n",
       "      <td>1.494870</td>\n",
       "      <td>1.970933</td>\n",
       "      <td>2.330075</td>\n",
       "      <td>1.676683</td>\n",
       "      <td>-0.125777</td>\n",
       "      <td>0.724950</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>0.611895</td>\n",
       "      <td>-0.655350</td>\n",
       "      <td>-0.499833</td>\n",
       "      <td>-2.156557</td>\n",
       "      <td>-2.970477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6196</th>\n",
       "      <td>-1.815814</td>\n",
       "      <td>0.273083</td>\n",
       "      <td>-2.951532</td>\n",
       "      <td>3.104686</td>\n",
       "      <td>0.981366</td>\n",
       "      <td>1.602459</td>\n",
       "      <td>2.125604</td>\n",
       "      <td>2.416907</td>\n",
       "      <td>1.457133</td>\n",
       "      <td>-0.151949</td>\n",
       "      <td>0.611140</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>1.191021</td>\n",
       "      <td>-0.735149</td>\n",
       "      <td>-0.820115</td>\n",
       "      <td>-2.190000</td>\n",
       "      <td>-2.842369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6197</th>\n",
       "      <td>-1.680513</td>\n",
       "      <td>0.353661</td>\n",
       "      <td>-3.003557</td>\n",
       "      <td>3.407620</td>\n",
       "      <td>0.675525</td>\n",
       "      <td>1.534201</td>\n",
       "      <td>2.312147</td>\n",
       "      <td>2.602183</td>\n",
       "      <td>2.024745</td>\n",
       "      <td>-0.092121</td>\n",
       "      <td>0.400733</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>-1.263971</td>\n",
       "      <td>-0.852681</td>\n",
       "      <td>-0.803915</td>\n",
       "      <td>-2.186347</td>\n",
       "      <td>-2.906423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6198</th>\n",
       "      <td>-1.680513</td>\n",
       "      <td>0.220089</td>\n",
       "      <td>-2.929177</td>\n",
       "      <td>3.410922</td>\n",
       "      <td>0.694940</td>\n",
       "      <td>1.498068</td>\n",
       "      <td>2.302101</td>\n",
       "      <td>2.556029</td>\n",
       "      <td>1.916674</td>\n",
       "      <td>-0.097121</td>\n",
       "      <td>0.454645</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-1.037356</td>\n",
       "      <td>-0.821213</td>\n",
       "      <td>-0.755095</td>\n",
       "      <td>-2.198020</td>\n",
       "      <td>-2.938450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6199</th>\n",
       "      <td>-1.883465</td>\n",
       "      <td>0.225614</td>\n",
       "      <td>-2.937813</td>\n",
       "      <td>3.387032</td>\n",
       "      <td>0.635237</td>\n",
       "      <td>1.499937</td>\n",
       "      <td>2.102797</td>\n",
       "      <td>2.516695</td>\n",
       "      <td>1.838733</td>\n",
       "      <td>-0.120700</td>\n",
       "      <td>0.500970</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-0.269384</td>\n",
       "      <td>-0.779043</td>\n",
       "      <td>-0.719338</td>\n",
       "      <td>-2.167465</td>\n",
       "      <td>-2.938450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7675</th>\n",
       "      <td>-2.221718</td>\n",
       "      <td>0.061175</td>\n",
       "      <td>-2.850518</td>\n",
       "      <td>3.165786</td>\n",
       "      <td>2.037223</td>\n",
       "      <td>0.014291</td>\n",
       "      <td>-1.059738</td>\n",
       "      <td>-1.063356</td>\n",
       "      <td>-0.784703</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>-1.511327</td>\n",
       "      <td>-3.034531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7700</th>\n",
       "      <td>-3.202651</td>\n",
       "      <td>-0.069554</td>\n",
       "      <td>-3.181468</td>\n",
       "      <td>-0.296906</td>\n",
       "      <td>0.184863</td>\n",
       "      <td>0.946993</td>\n",
       "      <td>1.139885</td>\n",
       "      <td>0.571433</td>\n",
       "      <td>-0.817978</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>-1.601879</td>\n",
       "      <td>-3.162640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7750</th>\n",
       "      <td>-3.304127</td>\n",
       "      <td>-4.113443</td>\n",
       "      <td>-4.087857</td>\n",
       "      <td>-1.939757</td>\n",
       "      <td>-2.267499</td>\n",
       "      <td>-1.412018</td>\n",
       "      <td>-1.386643</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-1.758184</td>\n",
       "      <td>-2.082302</td>\n",
       "      <td>-0.911963</td>\n",
       "      <td>-0.845455</td>\n",
       "      <td>-2.358212</td>\n",
       "      <td>-4.123453</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "2650     -2.086416     1.146897         -2.217884 -1.247235 -0.404519   \n",
       "2750     -2.086416     0.722336         -2.347124  0.720893  0.431804   \n",
       "5025     -1.443736     1.297482         -2.698896  3.154805  0.682660   \n",
       "5026     -0.564278     1.141629         -2.189783  0.265648 -0.153150   \n",
       "5050     -3.067350     1.585246         -2.476358  1.396136 -0.782847   \n",
       "6100     -1.646687     0.457765         -2.310616  1.833989  1.489480   \n",
       "6175     -2.695272     1.457979         -4.087857  4.911091 -0.210420   \n",
       "6176     -2.086416     0.517310         -3.535766  4.003889  1.463660   \n",
       "6177     -1.815814     0.293287         -3.323528  3.380546  0.732305   \n",
       "6178     -1.815814     1.381461         -3.854778  5.508391  1.053051   \n",
       "6179     -1.748163     0.540625         -3.180434  2.709573  0.667897   \n",
       "6180     -1.849639     0.291101         -3.008869  3.443116  0.875766   \n",
       "6181     -2.086416     0.726924         -3.416935  3.153251  1.033799   \n",
       "6182     -1.849639     0.128079         -2.987955  3.182614  0.539878   \n",
       "6183     -1.883465     0.559954         -3.090460  3.785644  0.588896   \n",
       "6184     -2.390844     0.548491         -3.356360  3.778144  0.340973   \n",
       "6185     -1.748163     0.073370         -2.977437  2.994312  0.760935   \n",
       "6186     -2.357019     0.397078         -3.309947  3.641401  0.469220   \n",
       "6187     -1.815814     0.203483         -3.346815  2.572588  0.430980   \n",
       "6188     -1.917290     0.196464         -2.927473  3.375313  0.684771   \n",
       "6189     -1.748163     0.335249         -3.121586  3.246297  0.445623   \n",
       "6190     -2.052591     0.342238         -3.133392  3.608837  0.689004   \n",
       "6191     -2.018766     0.443371         -3.559795  2.056200  0.187604   \n",
       "6193     -1.781989     0.683279         -3.546341  3.042051  0.351990   \n",
       "6194     -1.917290     1.544363         -4.016045  5.989302 -0.963308   \n",
       "6195     -1.984940     0.181415         -3.033073  3.594376  0.729578   \n",
       "6196     -1.815814     0.273083         -2.951532  3.104686  0.981366   \n",
       "6197     -1.680513     0.353661         -3.003557  3.407620  0.675525   \n",
       "6198     -1.680513     0.220089         -2.929177  3.410922  0.694940   \n",
       "6199     -1.883465     0.225614         -2.937813  3.387032  0.635237   \n",
       "7675     -2.221718     0.061175         -2.850518  3.165786  2.037223   \n",
       "7700     -3.202651    -0.069554         -3.181468 -0.296906  0.184863   \n",
       "7750     -3.304127    -4.113443         -4.087857 -1.939757 -2.267499   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "2650   1.025171   1.294520   0.671912   0.915515   -0.086042   -0.224453   \n",
       "2750   0.990499   1.166849   1.394751   2.292510   -0.188155    3.329386   \n",
       "5025   1.256502   1.666250   1.293021   0.393944   -0.067187    0.123279   \n",
       "5026   1.590055   1.613945   1.371150   0.589398   -0.085760    0.215901   \n",
       "5050   0.501491  -0.052399  -0.043444   0.101308    0.072443   -0.138037   \n",
       "6100   1.192236   0.211053   0.341799   1.351480   -0.204227    0.056343   \n",
       "6175   1.489658   2.281600   2.656311   2.268567   -0.017489    1.340116   \n",
       "6176   0.894764   1.277177   2.148085   1.600057    0.084708    2.187907   \n",
       "6177   1.483952   1.747604   2.166054   1.581048   -0.091321    1.378723   \n",
       "6178   1.129946   2.138966   2.557734   1.943433    0.196184    1.718829   \n",
       "6179   1.427806   2.207221   2.466783   1.705215   -0.208367    1.190854   \n",
       "6180   1.527562   2.186085   2.380435   1.732806   -0.111499    0.530213   \n",
       "6181   1.484045   2.193447   2.587556   2.013460   -0.123314    0.618552   \n",
       "6182   1.737777   2.324832   2.536665   1.386484    0.099575    0.452257   \n",
       "6183   1.794153   2.241939   2.621514   1.616357    0.058270    0.359513   \n",
       "6184   1.722154   2.379023   2.612524   1.583916    0.143382    0.585596   \n",
       "6185   1.561845   2.072250   2.185478   1.552148   -0.177150    0.826247   \n",
       "6186   1.452282   2.096269   2.496450   1.974161   -0.132036    0.559798   \n",
       "6187   1.597681   1.913452   2.183086   1.684987   -0.163895    1.556727   \n",
       "6188   1.560831   2.231691   2.590861   1.712657   -0.069786    0.474994   \n",
       "6189   1.492428   2.204697   2.533502   1.996110   -0.150345    0.469066   \n",
       "6190   1.649470   2.317912   2.603041   1.430013   -0.014496    0.546835   \n",
       "6191   1.580071   2.165352   2.341953   2.004631   -0.191814    2.098272   \n",
       "6193   1.475464   2.204002   2.544886   2.064549   -0.164838    0.821398   \n",
       "6194   1.394206   2.330022   2.670813   2.371793    0.122695    1.598140   \n",
       "6195   1.494870   1.970933   2.330075   1.676683   -0.125777    0.724950   \n",
       "6196   1.602459   2.125604   2.416907   1.457133   -0.151949    0.611140   \n",
       "6197   1.534201   2.312147   2.602183   2.024745   -0.092121    0.400733   \n",
       "6198   1.498068   2.302101   2.556029   1.916674   -0.097121    0.454645   \n",
       "6199   1.499937   2.102797   2.516695   1.838733   -0.120700    0.500970   \n",
       "7675   0.014291  -1.059738  -1.063356  -0.784703   -0.305750   -0.224453   \n",
       "7700   0.946993   1.139885   0.571433  -0.817978   -0.305750   -0.224453   \n",
       "7750  -1.412018  -1.386643  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  Next_Tmax  \n",
       "2650  1.189286 -0.005000  2.772243  1.115004        -0.295389  -2.938450  \n",
       "2750  1.189286 -0.005000  2.772243  1.115004        -0.592600  -3.002504  \n",
       "5025  1.189286 -0.005000  2.772243  1.115004         1.165416  -3.066558  \n",
       "5026  1.189286  0.511177 -0.315157 -0.542158         0.865895  -2.810342  \n",
       "5050  1.189286 -0.005000  2.772243  1.115004         1.131952  -3.066558  \n",
       "6100  1.189286 -0.005000  2.772243  1.115004        -1.511327  -3.034531  \n",
       "6175  1.189286 -0.005000  2.772243  1.115004        -1.786106  -4.123453  \n",
       "6176  1.189286  0.511177 -0.315157 -0.542158        -2.138584  -3.258721  \n",
       "6177  0.653021  0.838510 -0.526218 -0.723133        -2.150110  -3.194667  \n",
       "6178  1.991696  0.385280 -0.297588  0.932424        -2.174068  -3.098586  \n",
       "6179  0.118743  1.807917 -0.494322 -0.548433        -2.181193  -3.194667  \n",
       "6180 -0.685654  0.637074 -0.133199 -0.810993        -2.150112  -2.906423  \n",
       "6181  0.653021 -1.931225 -0.911963 -0.845437        -2.193176  -3.034531  \n",
       "6182 -1.490051 -1.024766 -0.172266  0.223191        -2.197299  -2.970477  \n",
       "6183 -0.953787 -2.082302 -0.201502 -0.616299        -2.121602  -3.066558  \n",
       "6184 -1.758184 -0.458230  2.701715  2.861413        -2.358212  -3.194667  \n",
       "6185 -0.149390  1.178432 -0.611095 -0.462470        -2.131853  -3.066558  \n",
       "6186  0.118743 -0.042770  1.294304 -0.484508        -2.046890  -3.739128  \n",
       "6187  0.653021  1.153252 -0.037504  1.043125        -2.208229  -3.130613  \n",
       "6188 -0.953787 -0.810742 -0.569309 -0.466338        -2.181012  -2.938450  \n",
       "6189  0.118743 -0.684844 -0.586289 -0.293244        -2.122811  -3.162640  \n",
       "6190 -1.490051  0.045358  0.376283  0.730359        -2.274529  -3.066558  \n",
       "6191  1.457418  1.354688 -0.154704 -0.408689        -2.146457  -3.386829  \n",
       "6193  0.653021 -0.672255  0.243650  0.372715        -2.064552  -3.643046  \n",
       "6194  1.457418  0.158666  1.560277  2.534052        -1.925129  -3.450884  \n",
       "6195  0.118743  0.611895 -0.655350 -0.499833        -2.156557  -2.970477  \n",
       "6196 -0.685654  1.191021 -0.735149 -0.820115        -2.190000  -2.842369  \n",
       "6197 -0.149390 -1.263971 -0.852681 -0.803915        -2.186347  -2.906423  \n",
       "6198 -0.417522 -1.037356 -0.821213 -0.755095        -2.198020  -2.938450  \n",
       "6199 -0.417522 -0.269384 -0.779043 -0.719338        -2.167465  -2.938450  \n",
       "7675  1.189286 -0.005000  2.772243  1.115004        -1.511327  -3.034531  \n",
       "7700  1.189286 -0.005000  2.772243  1.115004        -1.601879  -3.162640  \n",
       "7750 -1.758184 -2.082302 -0.911963 -0.845455        -2.358212  -4.123453  "
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yichang=Scaled_Data[Scaled_Data['Next_Tmax']<low_whisker] \n",
    "yichang"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:28:18.424085Z",
     "start_time": "2020-06-16T02:28:18.411090Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "      <td>-0.376282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "      <td>0.072097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "      <td>0.264260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "      <td>0.456422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "      <td>0.296287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7746</th>\n",
       "      <td>-2.458495</td>\n",
       "      <td>-0.654605</td>\n",
       "      <td>-0.991760</td>\n",
       "      <td>-0.611932</td>\n",
       "      <td>0.585186</td>\n",
       "      <td>-1.157541</td>\n",
       "      <td>-1.291164</td>\n",
       "      <td>-1.278051</td>\n",
       "      <td>-1.112273</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>1.191021</td>\n",
       "      <td>-0.735149</td>\n",
       "      <td>-0.820115</td>\n",
       "      <td>-2.096560</td>\n",
       "      <td>-0.728580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.328126</td>\n",
       "      <td>-1.112066</td>\n",
       "      <td>-0.436683</td>\n",
       "      <td>0.284622</td>\n",
       "      <td>-1.297018</td>\n",
       "      <td>-1.071078</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>-1.263971</td>\n",
       "      <td>-0.852681</td>\n",
       "      <td>-0.803915</td>\n",
       "      <td>-2.093040</td>\n",
       "      <td>-0.632499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.548184</td>\n",
       "      <td>-0.887662</td>\n",
       "      <td>-0.255421</td>\n",
       "      <td>-0.454749</td>\n",
       "      <td>-1.274658</td>\n",
       "      <td>-1.094726</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-1.037356</td>\n",
       "      <td>-0.821213</td>\n",
       "      <td>-0.755095</td>\n",
       "      <td>-2.104553</td>\n",
       "      <td>-0.536418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>-2.221718</td>\n",
       "      <td>-1.555342</td>\n",
       "      <td>-0.570780</td>\n",
       "      <td>0.088072</td>\n",
       "      <td>-1.591397</td>\n",
       "      <td>-1.224577</td>\n",
       "      <td>-1.153504</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.178973</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-0.269384</td>\n",
       "      <td>-0.779043</td>\n",
       "      <td>-0.719338</td>\n",
       "      <td>-2.074325</td>\n",
       "      <td>-0.792634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>2.649126</td>\n",
       "      <td>1.624409</td>\n",
       "      <td>3.044561</td>\n",
       "      <td>6.792009</td>\n",
       "      <td>4.496044</td>\n",
       "      <td>2.291644</td>\n",
       "      <td>2.384303</td>\n",
       "      <td>2.670813</td>\n",
       "      <td>2.669002</td>\n",
       "      <td>11.935477</td>\n",
       "      <td>13.651790</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>2.861435</td>\n",
       "      <td>1.517935</td>\n",
       "      <td>2.762374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7719 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "0        -0.361326     0.383078         -0.524889 -0.128382  0.206966   \n",
       "1         0.721084     0.311586          0.080895 -0.646994 -0.314841   \n",
       "2         0.619608    -0.614982          0.162936 -0.441604 -1.249283   \n",
       "3         0.754909     1.133054          0.031092 -0.666247  0.095997   \n",
       "4         0.551957     0.248765         -0.170325 -0.627154  1.354409   \n",
       "...            ...          ...               ...       ...       ...   \n",
       "7746     -2.458495    -0.654605         -0.991760 -0.611932  0.585186   \n",
       "7747     -2.187892    -1.328126         -1.112066 -0.436683  0.284622   \n",
       "7748     -2.187892    -1.548184         -0.887662 -0.255421 -0.454749   \n",
       "7749     -2.221718    -1.555342         -0.570780  0.088072 -1.591397   \n",
       "7751      2.649126     1.624409          3.044561  6.792009  4.496044   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0     -0.516243  -0.592636  -0.629013  -0.664815   -0.305750   -0.224453   \n",
       "1     -0.548557  -0.406199  -0.638055  -0.677462   -0.305750   -0.224453   \n",
       "2     -0.610450  -0.384009  -0.458843  -0.620575   -0.305750   -0.224453   \n",
       "3     -0.583539  -0.506548  -0.631178  -0.651696   -0.305750   -0.224453   \n",
       "4     -0.832287  -0.413115  -0.559990  -0.510358   -0.305750   -0.224453   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "7746  -1.157541  -1.291164  -1.278051  -1.112273   -0.305750   -0.224453   \n",
       "7747  -1.297018  -1.071078  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7748  -1.274658  -1.094726  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7749  -1.224577  -1.153504  -1.278054  -1.178973   -0.305750   -0.224453   \n",
       "7751   2.291644   2.384303   2.670813   2.669002   11.935477   13.651790   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  Next_Tmax  \n",
       "0     1.189286 -0.005000  2.772243  1.115004         1.517935  -0.376282  \n",
       "1     1.189286  0.511177 -0.315157 -0.542158         1.229950   0.072097  \n",
       "2     0.653021  0.838510 -0.526218 -0.723133         1.216534   0.264260  \n",
       "3     1.991696  0.385280 -0.297588  0.932424         1.201176   0.456422  \n",
       "4     0.118743  1.807917 -0.494322 -0.548433         1.207205   0.296287  \n",
       "...        ...       ...       ...       ...              ...        ...  \n",
       "7746 -0.685654  1.191021 -0.735149 -0.820115        -2.096560  -0.728580  \n",
       "7747 -0.149390 -1.263971 -0.852681 -0.803915        -2.093040  -0.632499  \n",
       "7748 -0.417522 -1.037356 -0.821213 -0.755095        -2.104553  -0.536418  \n",
       "7749 -0.417522 -0.269384 -0.779043 -0.719338        -2.074325  -0.792634  \n",
       "7751  1.991696  1.807917  2.772243  2.861435         1.517935   2.762374  \n",
       "\n",
       "[7719 rows x 17 columns]"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除掉Next_Tmax中小于下边缘值的记录（10分）\n",
    "Scaled_Data[Scaled_Data['Next_Tmax']>low_whisker] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:16:00.616309Z",
     "start_time": "2020-06-16T02:16:00.579332Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: Next_Tmax, dtype: float64)"
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Y中小于箱型图下边缘的值\n",
    "Y[Y<low_whisker]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:24:40.455480Z",
     "start_time": "2020-06-16T02:24:40.448484Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2650, 2750, 5025, 5026, 5050, 6100, 6175, 6176, 6177, 6178, 6179,\n",
       "       6180, 6181, 6182, 6183, 6184, 6185, 6186, 6187, 6188, 6189, 6190,\n",
       "       6191, 6193, 6194, 6195, 6196, 6197, 6198, 6199, 7675, 7700, 7750],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 要删除的样本的Index\n",
    "drop_index=Scaled_Data[Scaled_Data['Next_Tmax']<low_whisker].index.values\n",
    "drop_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:25:42.036217Z",
     "start_time": "2020-06-16T02:25:41.961262Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'[2650 2750 5025 5026 5050 6100 6175 6176 6177 6178 6179 6180 6181 6182\\n 6183 6184 6185 6186 6187 6188 6189 6190 6191 6193 6194 6195 6196 6197\\n 6198 6199 7675 7700 7750] not found in axis'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-387-e176447eee86>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# 删除Y中小于箱型图下边缘的值\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mX\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdrop_index\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mY\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mY\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdrop_index\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   4115\u001b[0m             \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4116\u001b[0m             \u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4117\u001b[1;33m             \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4118\u001b[0m         )\n\u001b[0;32m   4119\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   3912\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3913\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3914\u001b[1;33m                 \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3915\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3916\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[1;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[0;32m   3944\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3945\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3946\u001b[1;33m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3947\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3948\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   5338\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5339\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m\"ignore\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5340\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"{} not found in axis\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5341\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5342\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: '[2650 2750 5025 5026 5050 6100 6175 6176 6177 6178 6179 6180 6181 6182\\n 6183 6184 6185 6186 6187 6188 6189 6190 6191 6193 6194 6195 6196 6197\\n 6198 6199 7675 7700 7750] not found in axis'"
     ]
    }
   ],
   "source": [
    "# 删除Y中小于箱型图下边缘的值\n",
    "X=X.drop(index=drop_index)\n",
    "Y=Y.drop(index=drop_index) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:25:54.069979Z",
     "start_time": "2020-06-16T02:25:54.035982Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7746</th>\n",
       "      <td>-2.458495</td>\n",
       "      <td>-0.654605</td>\n",
       "      <td>-0.991760</td>\n",
       "      <td>-0.611932</td>\n",
       "      <td>0.585186</td>\n",
       "      <td>-1.157541</td>\n",
       "      <td>-1.291164</td>\n",
       "      <td>-1.278051</td>\n",
       "      <td>-1.112273</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>1.191021</td>\n",
       "      <td>-0.735149</td>\n",
       "      <td>-0.820115</td>\n",
       "      <td>-2.096560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.328126</td>\n",
       "      <td>-1.112066</td>\n",
       "      <td>-0.436683</td>\n",
       "      <td>0.284622</td>\n",
       "      <td>-1.297018</td>\n",
       "      <td>-1.071078</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>-1.263971</td>\n",
       "      <td>-0.852681</td>\n",
       "      <td>-0.803915</td>\n",
       "      <td>-2.093040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.548184</td>\n",
       "      <td>-0.887662</td>\n",
       "      <td>-0.255421</td>\n",
       "      <td>-0.454749</td>\n",
       "      <td>-1.274658</td>\n",
       "      <td>-1.094726</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-1.037356</td>\n",
       "      <td>-0.821213</td>\n",
       "      <td>-0.755095</td>\n",
       "      <td>-2.104553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>-2.221718</td>\n",
       "      <td>-1.555342</td>\n",
       "      <td>-0.570780</td>\n",
       "      <td>0.088072</td>\n",
       "      <td>-1.591397</td>\n",
       "      <td>-1.224577</td>\n",
       "      <td>-1.153504</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.178973</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-0.269384</td>\n",
       "      <td>-0.779043</td>\n",
       "      <td>-0.719338</td>\n",
       "      <td>-2.074325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>2.649126</td>\n",
       "      <td>1.624409</td>\n",
       "      <td>3.044561</td>\n",
       "      <td>6.792009</td>\n",
       "      <td>4.496044</td>\n",
       "      <td>2.291644</td>\n",
       "      <td>2.384303</td>\n",
       "      <td>2.670813</td>\n",
       "      <td>2.669002</td>\n",
       "      <td>11.935477</td>\n",
       "      <td>13.651790</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>2.861435</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7719 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "0        -0.361326     0.383078         -0.524889 -0.128382  0.206966   \n",
       "1         0.721084     0.311586          0.080895 -0.646994 -0.314841   \n",
       "2         0.619608    -0.614982          0.162936 -0.441604 -1.249283   \n",
       "3         0.754909     1.133054          0.031092 -0.666247  0.095997   \n",
       "4         0.551957     0.248765         -0.170325 -0.627154  1.354409   \n",
       "...            ...          ...               ...       ...       ...   \n",
       "7746     -2.458495    -0.654605         -0.991760 -0.611932  0.585186   \n",
       "7747     -2.187892    -1.328126         -1.112066 -0.436683  0.284622   \n",
       "7748     -2.187892    -1.548184         -0.887662 -0.255421 -0.454749   \n",
       "7749     -2.221718    -1.555342         -0.570780  0.088072 -1.591397   \n",
       "7751      2.649126     1.624409          3.044561  6.792009  4.496044   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0     -0.516243  -0.592636  -0.629013  -0.664815   -0.305750   -0.224453   \n",
       "1     -0.548557  -0.406199  -0.638055  -0.677462   -0.305750   -0.224453   \n",
       "2     -0.610450  -0.384009  -0.458843  -0.620575   -0.305750   -0.224453   \n",
       "3     -0.583539  -0.506548  -0.631178  -0.651696   -0.305750   -0.224453   \n",
       "4     -0.832287  -0.413115  -0.559990  -0.510358   -0.305750   -0.224453   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "7746  -1.157541  -1.291164  -1.278051  -1.112273   -0.305750   -0.224453   \n",
       "7747  -1.297018  -1.071078  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7748  -1.274658  -1.094726  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7749  -1.224577  -1.153504  -1.278054  -1.178973   -0.305750   -0.224453   \n",
       "7751   2.291644   2.384303   2.670813   2.669002   11.935477   13.651790   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  \n",
       "0     1.189286 -0.005000  2.772243  1.115004         1.517935  \n",
       "1     1.189286  0.511177 -0.315157 -0.542158         1.229950  \n",
       "2     0.653021  0.838510 -0.526218 -0.723133         1.216534  \n",
       "3     1.991696  0.385280 -0.297588  0.932424         1.201176  \n",
       "4     0.118743  1.807917 -0.494322 -0.548433         1.207205  \n",
       "...        ...       ...       ...       ...              ...  \n",
       "7746 -0.685654  1.191021 -0.735149 -0.820115        -2.096560  \n",
       "7747 -0.149390 -1.263971 -0.852681 -0.803915        -2.093040  \n",
       "7748 -0.417522 -1.037356 -0.821213 -0.755095        -2.104553  \n",
       "7749 -0.417522 -0.269384 -0.779043 -0.719338        -2.074325  \n",
       "7751  1.991696  1.807917  2.772243  2.861435         1.517935  \n",
       "\n",
       "[7719 rows x 16 columns]"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0      -0.376282\n",
       "1       0.072097\n",
       "2       0.264260\n",
       "3       0.456422\n",
       "4       0.296287\n",
       "          ...   \n",
       "7746   -0.728580\n",
       "7747   -0.632499\n",
       "7748   -0.536418\n",
       "7749   -0.792634\n",
       "7751    2.762374\n",
       "Name: Next_Tmax, Length: 7719, dtype: float64"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除后的X,Y  \n",
    "X \n",
    "Y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:21:08.371115Z",
     "start_time": "2020-06-16T03:21:08.337137Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.2365368942338733\n",
      "RMSE: 0.48635058777991963\n"
     ]
    }
   ],
   "source": [
    "# 重新划分数据集\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=1)\n",
    "# 对训练集进行训练\n",
    "model=lr()\n",
    "model.fit(x_train, y_train) \n",
    "# 对测试集进行预测\n",
    "y_pred = model.predict(x_test)\n",
    "MSE = mean_squared_error(y_test, y_pred)\n",
    "RMSE = np.sqrt(MSE) \n",
    "print('MSE:',MSE) \n",
    "print('RMSE:',RMSE)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#上一次评估结果 \n",
    "# MSE :0.2554011051627178 \n",
    "#RMSE :0.4928285377503317"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:27.917423Z",
     "start_time": "2020-06-16T00:15:27.728011Z"
    }
   },
   "source": [
    "'''\n",
    "可以看到，该模型的RMSE由原来的0.5降低到了0.48，小幅度的提升了模型的准确率;去除异常值，提高准确度。\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:27.527723Z",
     "start_time": "2020-06-16T03:22:27.496745Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.328126</td>\n",
       "      <td>-1.112066</td>\n",
       "      <td>-0.436683</td>\n",
       "      <td>0.284622</td>\n",
       "      <td>-1.297018</td>\n",
       "      <td>-1.071078</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>-1.263971</td>\n",
       "      <td>-0.852681</td>\n",
       "      <td>-0.803915</td>\n",
       "      <td>-2.093040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.548184</td>\n",
       "      <td>-0.887662</td>\n",
       "      <td>-0.255421</td>\n",
       "      <td>-0.454749</td>\n",
       "      <td>-1.274658</td>\n",
       "      <td>-1.094726</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-1.037356</td>\n",
       "      <td>-0.821213</td>\n",
       "      <td>-0.755095</td>\n",
       "      <td>-2.104553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>-2.221718</td>\n",
       "      <td>-1.555342</td>\n",
       "      <td>-0.570780</td>\n",
       "      <td>0.088072</td>\n",
       "      <td>-1.591397</td>\n",
       "      <td>-1.224577</td>\n",
       "      <td>-1.153504</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.178973</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-0.269384</td>\n",
       "      <td>-0.779043</td>\n",
       "      <td>-0.719338</td>\n",
       "      <td>-2.074325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7750</th>\n",
       "      <td>-3.304127</td>\n",
       "      <td>-4.113443</td>\n",
       "      <td>-4.087857</td>\n",
       "      <td>-1.939757</td>\n",
       "      <td>-2.267499</td>\n",
       "      <td>-1.412018</td>\n",
       "      <td>-1.386643</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-1.758184</td>\n",
       "      <td>-2.082302</td>\n",
       "      <td>-0.911963</td>\n",
       "      <td>-0.845455</td>\n",
       "      <td>-2.358212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>2.649126</td>\n",
       "      <td>1.624409</td>\n",
       "      <td>3.044561</td>\n",
       "      <td>6.792009</td>\n",
       "      <td>4.496044</td>\n",
       "      <td>2.291644</td>\n",
       "      <td>2.384303</td>\n",
       "      <td>2.670813</td>\n",
       "      <td>2.669002</td>\n",
       "      <td>11.935477</td>\n",
       "      <td>13.651790</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>2.861435</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7752 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "0        -0.361326     0.383078         -0.524889 -0.128382  0.206966   \n",
       "1         0.721084     0.311586          0.080895 -0.646994 -0.314841   \n",
       "2         0.619608    -0.614982          0.162936 -0.441604 -1.249283   \n",
       "3         0.754909     1.133054          0.031092 -0.666247  0.095997   \n",
       "4         0.551957     0.248765         -0.170325 -0.627154  1.354409   \n",
       "...            ...          ...               ...       ...       ...   \n",
       "7747     -2.187892    -1.328126         -1.112066 -0.436683  0.284622   \n",
       "7748     -2.187892    -1.548184         -0.887662 -0.255421 -0.454749   \n",
       "7749     -2.221718    -1.555342         -0.570780  0.088072 -1.591397   \n",
       "7750     -3.304127    -4.113443         -4.087857 -1.939757 -2.267499   \n",
       "7751      2.649126     1.624409          3.044561  6.792009  4.496044   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0     -0.516243  -0.592636  -0.629013  -0.664815   -0.305750   -0.224453   \n",
       "1     -0.548557  -0.406199  -0.638055  -0.677462   -0.305750   -0.224453   \n",
       "2     -0.610450  -0.384009  -0.458843  -0.620575   -0.305750   -0.224453   \n",
       "3     -0.583539  -0.506548  -0.631178  -0.651696   -0.305750   -0.224453   \n",
       "4     -0.832287  -0.413115  -0.559990  -0.510358   -0.305750   -0.224453   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "7747  -1.297018  -1.071078  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7748  -1.274658  -1.094726  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7749  -1.224577  -1.153504  -1.278054  -1.178973   -0.305750   -0.224453   \n",
       "7750  -1.412018  -1.386643  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7751   2.291644   2.384303   2.670813   2.669002   11.935477   13.651790   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  \n",
       "0     1.189286 -0.005000  2.772243  1.115004         1.517935  \n",
       "1     1.189286  0.511177 -0.315157 -0.542158         1.229950  \n",
       "2     0.653021  0.838510 -0.526218 -0.723133         1.216534  \n",
       "3     1.991696  0.385280 -0.297588  0.932424         1.201176  \n",
       "4     0.118743  1.807917 -0.494322 -0.548433         1.207205  \n",
       "...        ...       ...       ...       ...              ...  \n",
       "7747 -0.149390 -1.263971 -0.852681 -0.803915        -2.093040  \n",
       "7748 -0.417522 -1.037356 -0.821213 -0.755095        -2.104553  \n",
       "7749 -0.417522 -0.269384 -0.779043 -0.719338        -2.074325  \n",
       "7750 -1.758184 -2.082302 -0.911963 -0.845455        -2.358212  \n",
       "7751  1.991696  1.807917  2.772243  2.861435         1.517935  \n",
       "\n",
       "[7752 rows x 16 columns]"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:30.962802Z",
     "start_time": "2020-06-16T03:22:30.906836Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 特征筛选，选出和样本标签之间存在显著相关性（p<0.05)的特征，从而过滤掉对模型预测贡献不大的特征\n",
    "from sklearn.feature_selection import f_regression\n",
    "\n",
    "F,p=f_regression(X,Y)  # 一个特征对应一个F值和p值\n",
    "k=F.shape[0] - (p>0.05).sum() # 去掉p值<=0.05的特征，k就是剩下的特征数量\n",
    "k "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:33.930084Z",
     "start_time": "2020-06-16T03:22:33.914094Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7750, 14)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.36132577,  0.38307796, -0.52488856, ...,  1.18928568,\n",
       "         2.77224286,  1.11500407],\n",
       "       [ 0.72108401,  0.31158619,  0.08089519, ...,  1.18928568,\n",
       "        -0.31515742, -0.54215762],\n",
       "       [ 0.61960809, -0.61498177,  0.16293647, ...,  0.65302103,\n",
       "        -0.52621832, -0.7231326 ],\n",
       "       ...,\n",
       "       [-2.18789227, -1.54818379, -0.88766178, ..., -0.41752209,\n",
       "        -0.82121274, -0.75509512],\n",
       "       [-2.22171758, -1.55534234, -0.57077984, ..., -0.41752209,\n",
       "        -0.77904331, -0.71933797],\n",
       "       [ 2.64912642,  1.62440928,  3.04456067, ...,  1.99169648,\n",
       "         2.77224286,  2.86143459]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# F检验筛选特征\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "\n",
    "X_f=SelectKBest(f_regression,k=k).fit_transform(X,Y) \n",
    "X_f.shape    # 过滤掉了两个特征\n",
    "X_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:38.343703Z",
     "start_time": "2020-06-16T03:22:38.336692Z"
    }
   },
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest=train_test_split(X_f,Y,test_size=0.3, random_state=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:40.510995Z",
     "start_time": "2020-06-16T03:22:40.504012Z"
    }
   },
   "outputs": [],
   "source": [
    "# # 实例化并训练模型\n",
    "lr=LinearRegression().fit(xtrain,ytrain) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:45.574369Z",
     "start_time": "2020-06-16T03:22:45.562377Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7455182172970509, 0.759486375370554)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 测试集上的评分（R²）\n",
    "lr.score(xtrain,ytrain),lr.score(xtest,ytest) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:50.126024Z",
     "start_time": "2020-06-16T03:22:50.117031Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.23443534187388376\n",
      "RMSE: 0.4841852350845529\n"
     ]
    }
   ],
   "source": [
    "ypred = lr.predict(xtest) \n",
    "MSE = metrics.mean_squared_error(ytest, ypred)\n",
    "RMSE = np.sqrt(metrics.mean_squared_error(ytest, ypred)) \n",
    "print('MSE:',MSE) \n",
    "print('RMSE:',RMSE) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA降维\n",
    "\n",
    "主成分分析的目的是从现有的特征中重建新的特征，新的特征剔除了原有特征中的冗余信息，因此更有区分度。\n",
    "\n",
    "主成分分析的结果是得到新的特征，而不是简单地舍弃原来的特征列表中的一些特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:58.610715Z",
     "start_time": "2020-06-16T03:22:58.587725Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca=PCA(n_components=0.97).fit(X,Y) \n",
    "X_pca=pca.transform(X)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:04.873414Z",
     "start_time": "2020-06-16T03:23:04.832436Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.216577</td>\n",
       "      <td>3.103277</td>\n",
       "      <td>-0.110833</td>\n",
       "      <td>0.165686</td>\n",
       "      <td>0.790641</td>\n",
       "      <td>-0.681205</td>\n",
       "      <td>-1.317347</td>\n",
       "      <td>-0.238028</td>\n",
       "      <td>-0.203376</td>\n",
       "      <td>0.603971</td>\n",
       "      <td>-0.337200</td>\n",
       "      <td>0.358007</td>\n",
       "      <td>-0.423250</td>\n",
       "      <td>1.099815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.090162</td>\n",
       "      <td>-0.107501</td>\n",
       "      <td>1.262260</td>\n",
       "      <td>0.415918</td>\n",
       "      <td>0.528656</td>\n",
       "      <td>-0.648804</td>\n",
       "      <td>-1.089290</td>\n",
       "      <td>-0.077244</td>\n",
       "      <td>-0.327796</td>\n",
       "      <td>0.455522</td>\n",
       "      <td>0.200883</td>\n",
       "      <td>-0.248300</td>\n",
       "      <td>-0.819938</td>\n",
       "      <td>0.253945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.170936</td>\n",
       "      <td>-0.890398</td>\n",
       "      <td>0.501055</td>\n",
       "      <td>0.423675</td>\n",
       "      <td>0.775087</td>\n",
       "      <td>-1.372693</td>\n",
       "      <td>-0.972450</td>\n",
       "      <td>0.227718</td>\n",
       "      <td>0.223689</td>\n",
       "      <td>0.190134</td>\n",
       "      <td>0.099512</td>\n",
       "      <td>-0.201590</td>\n",
       "      <td>-0.922498</td>\n",
       "      <td>0.184477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.834243</td>\n",
       "      <td>1.217238</td>\n",
       "      <td>1.663132</td>\n",
       "      <td>0.726037</td>\n",
       "      <td>0.587636</td>\n",
       "      <td>-0.459220</td>\n",
       "      <td>-0.996600</td>\n",
       "      <td>-0.280367</td>\n",
       "      <td>-0.815869</td>\n",
       "      <td>0.995221</td>\n",
       "      <td>0.419671</td>\n",
       "      <td>-0.486277</td>\n",
       "      <td>-0.841180</td>\n",
       "      <td>-0.672248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.214938</td>\n",
       "      <td>0.125820</td>\n",
       "      <td>1.699884</td>\n",
       "      <td>0.871620</td>\n",
       "      <td>0.306261</td>\n",
       "      <td>0.069075</td>\n",
       "      <td>-0.711520</td>\n",
       "      <td>-0.439632</td>\n",
       "      <td>-0.165456</td>\n",
       "      <td>-1.432835</td>\n",
       "      <td>-0.681152</td>\n",
       "      <td>-0.127686</td>\n",
       "      <td>-0.932863</td>\n",
       "      <td>0.190224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7745</th>\n",
       "      <td>-1.786437</td>\n",
       "      <td>0.231800</td>\n",
       "      <td>-0.034922</td>\n",
       "      <td>0.032411</td>\n",
       "      <td>-3.623162</td>\n",
       "      <td>-0.618894</td>\n",
       "      <td>0.170518</td>\n",
       "      <td>0.229131</td>\n",
       "      <td>1.534717</td>\n",
       "      <td>-1.291132</td>\n",
       "      <td>-0.853182</td>\n",
       "      <td>-0.036252</td>\n",
       "      <td>-0.137269</td>\n",
       "      <td>-0.045560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7746</th>\n",
       "      <td>-1.871831</td>\n",
       "      <td>-0.153308</td>\n",
       "      <td>-1.028628</td>\n",
       "      <td>-0.810918</td>\n",
       "      <td>-3.635659</td>\n",
       "      <td>0.140778</td>\n",
       "      <td>-0.260850</td>\n",
       "      <td>0.833126</td>\n",
       "      <td>0.884765</td>\n",
       "      <td>0.511711</td>\n",
       "      <td>-0.727532</td>\n",
       "      <td>0.529375</td>\n",
       "      <td>-0.279357</td>\n",
       "      <td>-0.127452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-1.904300</td>\n",
       "      <td>-0.383570</td>\n",
       "      <td>-1.415379</td>\n",
       "      <td>-0.856730</td>\n",
       "      <td>-3.348666</td>\n",
       "      <td>-0.376563</td>\n",
       "      <td>-0.161274</td>\n",
       "      <td>0.946401</td>\n",
       "      <td>1.242176</td>\n",
       "      <td>0.431955</td>\n",
       "      <td>-0.515940</td>\n",
       "      <td>0.329307</td>\n",
       "      <td>-0.214308</td>\n",
       "      <td>-0.169483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-1.829787</td>\n",
       "      <td>-0.559760</td>\n",
       "      <td>-1.535874</td>\n",
       "      <td>-0.621879</td>\n",
       "      <td>-2.943078</td>\n",
       "      <td>-1.374214</td>\n",
       "      <td>-0.052400</td>\n",
       "      <td>1.204855</td>\n",
       "      <td>1.792964</td>\n",
       "      <td>0.294714</td>\n",
       "      <td>-0.109837</td>\n",
       "      <td>-0.169241</td>\n",
       "      <td>-0.115426</td>\n",
       "      <td>-0.188458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>8.725825</td>\n",
       "      <td>3.056087</td>\n",
       "      <td>4.609578</td>\n",
       "      <td>4.595430</td>\n",
       "      <td>8.330166</td>\n",
       "      <td>9.435757</td>\n",
       "      <td>5.962135</td>\n",
       "      <td>0.194339</td>\n",
       "      <td>10.953023</td>\n",
       "      <td>3.910947</td>\n",
       "      <td>0.153466</td>\n",
       "      <td>1.686319</td>\n",
       "      <td>-0.332968</td>\n",
       "      <td>0.823091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7750 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0         1         2         3         4         5         6  \\\n",
       "0    -0.216577  3.103277 -0.110833  0.165686  0.790641 -0.681205 -1.317347   \n",
       "1    -1.090162 -0.107501  1.262260  0.415918  0.528656 -0.648804 -1.089290   \n",
       "2    -1.170936 -0.890398  0.501055  0.423675  0.775087 -1.372693 -0.972450   \n",
       "3    -0.834243  1.217238  1.663132  0.726037  0.587636 -0.459220 -0.996600   \n",
       "4    -1.214938  0.125820  1.699884  0.871620  0.306261  0.069075 -0.711520   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "7745 -1.786437  0.231800 -0.034922  0.032411 -3.623162 -0.618894  0.170518   \n",
       "7746 -1.871831 -0.153308 -1.028628 -0.810918 -3.635659  0.140778 -0.260850   \n",
       "7747 -1.904300 -0.383570 -1.415379 -0.856730 -3.348666 -0.376563 -0.161274   \n",
       "7748 -1.829787 -0.559760 -1.535874 -0.621879 -2.943078 -1.374214 -0.052400   \n",
       "7749  8.725825  3.056087  4.609578  4.595430  8.330166  9.435757  5.962135   \n",
       "\n",
       "             7          8         9        10        11        12        13  \n",
       "0    -0.238028  -0.203376  0.603971 -0.337200  0.358007 -0.423250  1.099815  \n",
       "1    -0.077244  -0.327796  0.455522  0.200883 -0.248300 -0.819938  0.253945  \n",
       "2     0.227718   0.223689  0.190134  0.099512 -0.201590 -0.922498  0.184477  \n",
       "3    -0.280367  -0.815869  0.995221  0.419671 -0.486277 -0.841180 -0.672248  \n",
       "4    -0.439632  -0.165456 -1.432835 -0.681152 -0.127686 -0.932863  0.190224  \n",
       "...        ...        ...       ...       ...       ...       ...       ...  \n",
       "7745  0.229131   1.534717 -1.291132 -0.853182 -0.036252 -0.137269 -0.045560  \n",
       "7746  0.833126   0.884765  0.511711 -0.727532  0.529375 -0.279357 -0.127452  \n",
       "7747  0.946401   1.242176  0.431955 -0.515940  0.329307 -0.214308 -0.169483  \n",
       "7748  1.204855   1.792964  0.294714 -0.109837 -0.169241 -0.115426 -0.188458  \n",
       "7749  0.194339  10.953023  3.910947  0.153466  1.686319 -0.332968  0.823091  \n",
       "\n",
       "[7750 rows x 14 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pca=pd.DataFrame(X_pca)\n",
    "X_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:07.297511Z",
     "start_time": "2020-06-16T03:23:07.288516Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      -0.376282\n",
       "1       0.072097\n",
       "2       0.264260\n",
       "3       0.456422\n",
       "4       0.296287\n",
       "          ...   \n",
       "7746   -0.728580\n",
       "7747   -0.632499\n",
       "7748   -0.536418\n",
       "7749   -0.792634\n",
       "7751    2.762374\n",
       "Name: Next_Tmax, Length: 7750, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:10.305510Z",
     "start_time": "2020-06-16T03:23:10.277525Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.228159</td>\n",
       "      <td>0.259139</td>\n",
       "      <td>-0.363300</td>\n",
       "      <td>0.214156</td>\n",
       "      <td>-0.098169</td>\n",
       "      <td>0.389183</td>\n",
       "      <td>0.420652</td>\n",
       "      <td>0.400378</td>\n",
       "      <td>0.343841</td>\n",
       "      <td>0.192053</td>\n",
       "      <td>0.162411</td>\n",
       "      <td>0.025265</td>\n",
       "      <td>-0.008065</td>\n",
       "      <td>0.076506</td>\n",
       "      <td>0.069054</td>\n",
       "      <td>0.108524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.177762</td>\n",
       "      <td>0.259298</td>\n",
       "      <td>-0.115402</td>\n",
       "      <td>0.139626</td>\n",
       "      <td>0.253892</td>\n",
       "      <td>-0.080516</td>\n",
       "      <td>-0.148532</td>\n",
       "      <td>-0.188108</td>\n",
       "      <td>-0.188080</td>\n",
       "      <td>0.000994</td>\n",
       "      <td>-0.060552</td>\n",
       "      <td>0.175085</td>\n",
       "      <td>0.061905</td>\n",
       "      <td>0.573748</td>\n",
       "      <td>0.580675</td>\n",
       "      <td>0.008013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.041856</td>\n",
       "      <td>0.375808</td>\n",
       "      <td>0.133046</td>\n",
       "      <td>-0.096357</td>\n",
       "      <td>0.346909</td>\n",
       "      <td>0.204690</td>\n",
       "      <td>0.055840</td>\n",
       "      <td>-0.139376</td>\n",
       "      <td>-0.192081</td>\n",
       "      <td>0.314158</td>\n",
       "      <td>-0.091826</td>\n",
       "      <td>0.459231</td>\n",
       "      <td>0.361806</td>\n",
       "      <td>-0.282789</td>\n",
       "      <td>-0.231722</td>\n",
       "      <td>0.160499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.128714</td>\n",
       "      <td>-0.109038</td>\n",
       "      <td>-0.004835</td>\n",
       "      <td>-0.019345</td>\n",
       "      <td>0.086365</td>\n",
       "      <td>-0.269335</td>\n",
       "      <td>-0.083357</td>\n",
       "      <td>0.229271</td>\n",
       "      <td>0.328601</td>\n",
       "      <td>-0.366989</td>\n",
       "      <td>0.459058</td>\n",
       "      <td>0.392905</td>\n",
       "      <td>0.459881</td>\n",
       "      <td>0.010425</td>\n",
       "      <td>0.048139</td>\n",
       "      <td>-0.099735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.521499</td>\n",
       "      <td>-0.132052</td>\n",
       "      <td>0.415585</td>\n",
       "      <td>0.200768</td>\n",
       "      <td>-0.109847</td>\n",
       "      <td>0.064168</td>\n",
       "      <td>0.077508</td>\n",
       "      <td>0.086216</td>\n",
       "      <td>0.099563</td>\n",
       "      <td>0.174102</td>\n",
       "      <td>0.008157</td>\n",
       "      <td>-0.070494</td>\n",
       "      <td>0.084456</td>\n",
       "      <td>0.188751</td>\n",
       "      <td>0.208160</td>\n",
       "      <td>0.580179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.234427</td>\n",
       "      <td>0.185108</td>\n",
       "      <td>-0.007308</td>\n",
       "      <td>0.228682</td>\n",
       "      <td>0.630832</td>\n",
       "      <td>-0.071018</td>\n",
       "      <td>-0.105731</td>\n",
       "      <td>-0.007234</td>\n",
       "      <td>0.102064</td>\n",
       "      <td>0.046327</td>\n",
       "      <td>0.409067</td>\n",
       "      <td>-0.213486</td>\n",
       "      <td>-0.445201</td>\n",
       "      <td>-0.111096</td>\n",
       "      <td>-0.084715</td>\n",
       "      <td>-0.031756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.377125</td>\n",
       "      <td>-0.085739</td>\n",
       "      <td>0.078499</td>\n",
       "      <td>0.002232</td>\n",
       "      <td>0.043436</td>\n",
       "      <td>0.071895</td>\n",
       "      <td>0.110212</td>\n",
       "      <td>0.108762</td>\n",
       "      <td>0.049060</td>\n",
       "      <td>0.444529</td>\n",
       "      <td>-0.093587</td>\n",
       "      <td>-0.110817</td>\n",
       "      <td>0.207857</td>\n",
       "      <td>0.087366</td>\n",
       "      <td>0.163401</td>\n",
       "      <td>-0.717383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.013714</td>\n",
       "      <td>-0.274986</td>\n",
       "      <td>-0.115457</td>\n",
       "      <td>0.835690</td>\n",
       "      <td>-0.034486</td>\n",
       "      <td>-0.002497</td>\n",
       "      <td>-0.048047</td>\n",
       "      <td>-0.071086</td>\n",
       "      <td>-0.092055</td>\n",
       "      <td>-0.053476</td>\n",
       "      <td>-0.223025</td>\n",
       "      <td>0.311199</td>\n",
       "      <td>-0.028485</td>\n",
       "      <td>-0.119224</td>\n",
       "      <td>-0.142026</td>\n",
       "      <td>-0.103987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.370291</td>\n",
       "      <td>-0.141150</td>\n",
       "      <td>0.121759</td>\n",
       "      <td>0.177218</td>\n",
       "      <td>-0.215333</td>\n",
       "      <td>-0.003833</td>\n",
       "      <td>-0.205263</td>\n",
       "      <td>-0.251031</td>\n",
       "      <td>-0.129045</td>\n",
       "      <td>0.434367</td>\n",
       "      <td>0.584006</td>\n",
       "      <td>-0.194281</td>\n",
       "      <td>0.238188</td>\n",
       "      <td>0.003956</td>\n",
       "      <td>-0.061626</td>\n",
       "      <td>0.022683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.042747</td>\n",
       "      <td>-0.017327</td>\n",
       "      <td>0.098680</td>\n",
       "      <td>-0.178946</td>\n",
       "      <td>-0.295307</td>\n",
       "      <td>-0.123886</td>\n",
       "      <td>-0.067586</td>\n",
       "      <td>0.010266</td>\n",
       "      <td>0.076458</td>\n",
       "      <td>0.310972</td>\n",
       "      <td>0.161651</td>\n",
       "      <td>0.616572</td>\n",
       "      <td>-0.574300</td>\n",
       "      <td>0.036589</td>\n",
       "      <td>0.062666</td>\n",
       "      <td>-0.070417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.228455</td>\n",
       "      <td>0.371271</td>\n",
       "      <td>0.185369</td>\n",
       "      <td>0.123466</td>\n",
       "      <td>-0.328695</td>\n",
       "      <td>0.234673</td>\n",
       "      <td>0.281510</td>\n",
       "      <td>-0.140353</td>\n",
       "      <td>-0.417383</td>\n",
       "      <td>-0.404841</td>\n",
       "      <td>0.328316</td>\n",
       "      <td>0.009849</td>\n",
       "      <td>-0.060252</td>\n",
       "      <td>-0.034421</td>\n",
       "      <td>0.054214</td>\n",
       "      <td>-0.211079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.034273</td>\n",
       "      <td>-0.637297</td>\n",
       "      <td>-0.063050</td>\n",
       "      <td>-0.208273</td>\n",
       "      <td>0.323071</td>\n",
       "      <td>0.465238</td>\n",
       "      <td>0.257615</td>\n",
       "      <td>-0.075044</td>\n",
       "      <td>-0.237589</td>\n",
       "      <td>-0.093466</td>\n",
       "      <td>0.184750</td>\n",
       "      <td>0.156335</td>\n",
       "      <td>-0.079597</td>\n",
       "      <td>0.142051</td>\n",
       "      <td>0.039583</td>\n",
       "      <td>0.025827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.352815</td>\n",
       "      <td>0.057000</td>\n",
       "      <td>0.671374</td>\n",
       "      <td>0.083665</td>\n",
       "      <td>0.102315</td>\n",
       "      <td>0.319565</td>\n",
       "      <td>-0.077398</td>\n",
       "      <td>-0.062434</td>\n",
       "      <td>0.416761</td>\n",
       "      <td>-0.181899</td>\n",
       "      <td>-0.123503</td>\n",
       "      <td>0.028882</td>\n",
       "      <td>-0.074246</td>\n",
       "      <td>0.140435</td>\n",
       "      <td>-0.075031</td>\n",
       "      <td>-0.195759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.048350</td>\n",
       "      <td>0.064477</td>\n",
       "      <td>0.013980</td>\n",
       "      <td>0.004915</td>\n",
       "      <td>0.015767</td>\n",
       "      <td>-0.187600</td>\n",
       "      <td>0.140001</td>\n",
       "      <td>0.123015</td>\n",
       "      <td>-0.138519</td>\n",
       "      <td>0.039964</td>\n",
       "      <td>0.012322</td>\n",
       "      <td>0.013607</td>\n",
       "      <td>0.012477</td>\n",
       "      <td>0.666108</td>\n",
       "      <td>-0.676660</td>\n",
       "      <td>-0.016238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6  \\\n",
       "0  -0.228159  0.259139 -0.363300  0.214156 -0.098169  0.389183  0.420652   \n",
       "1  -0.177762  0.259298 -0.115402  0.139626  0.253892 -0.080516 -0.148532   \n",
       "2   0.041856  0.375808  0.133046 -0.096357  0.346909  0.204690  0.055840   \n",
       "3   0.128714 -0.109038 -0.004835 -0.019345  0.086365 -0.269335 -0.083357   \n",
       "4   0.521499 -0.132052  0.415585  0.200768 -0.109847  0.064168  0.077508   \n",
       "5   0.234427  0.185108 -0.007308  0.228682  0.630832 -0.071018 -0.105731   \n",
       "6   0.377125 -0.085739  0.078499  0.002232  0.043436  0.071895  0.110212   \n",
       "7   0.013714 -0.274986 -0.115457  0.835690 -0.034486 -0.002497 -0.048047   \n",
       "8  -0.370291 -0.141150  0.121759  0.177218 -0.215333 -0.003833 -0.205263   \n",
       "9   0.042747 -0.017327  0.098680 -0.178946 -0.295307 -0.123886 -0.067586   \n",
       "10  0.228455  0.371271  0.185369  0.123466 -0.328695  0.234673  0.281510   \n",
       "11 -0.034273 -0.637297 -0.063050 -0.208273  0.323071  0.465238  0.257615   \n",
       "12 -0.352815  0.057000  0.671374  0.083665  0.102315  0.319565 -0.077398   \n",
       "13  0.048350  0.064477  0.013980  0.004915  0.015767 -0.187600  0.140001   \n",
       "\n",
       "           7         8         9        10        11        12        13  \\\n",
       "0   0.400378  0.343841  0.192053  0.162411  0.025265 -0.008065  0.076506   \n",
       "1  -0.188108 -0.188080  0.000994 -0.060552  0.175085  0.061905  0.573748   \n",
       "2  -0.139376 -0.192081  0.314158 -0.091826  0.459231  0.361806 -0.282789   \n",
       "3   0.229271  0.328601 -0.366989  0.459058  0.392905  0.459881  0.010425   \n",
       "4   0.086216  0.099563  0.174102  0.008157 -0.070494  0.084456  0.188751   \n",
       "5  -0.007234  0.102064  0.046327  0.409067 -0.213486 -0.445201 -0.111096   \n",
       "6   0.108762  0.049060  0.444529 -0.093587 -0.110817  0.207857  0.087366   \n",
       "7  -0.071086 -0.092055 -0.053476 -0.223025  0.311199 -0.028485 -0.119224   \n",
       "8  -0.251031 -0.129045  0.434367  0.584006 -0.194281  0.238188  0.003956   \n",
       "9   0.010266  0.076458  0.310972  0.161651  0.616572 -0.574300  0.036589   \n",
       "10 -0.140353 -0.417383 -0.404841  0.328316  0.009849 -0.060252 -0.034421   \n",
       "11 -0.075044 -0.237589 -0.093466  0.184750  0.156335 -0.079597  0.142051   \n",
       "12 -0.062434  0.416761 -0.181899 -0.123503  0.028882 -0.074246  0.140435   \n",
       "13  0.123015 -0.138519  0.039964  0.012322  0.013607  0.012477  0.666108   \n",
       "\n",
       "          14        15  \n",
       "0   0.069054  0.108524  \n",
       "1   0.580675  0.008013  \n",
       "2  -0.231722  0.160499  \n",
       "3   0.048139 -0.099735  \n",
       "4   0.208160  0.580179  \n",
       "5  -0.084715 -0.031756  \n",
       "6   0.163401 -0.717383  \n",
       "7  -0.142026 -0.103987  \n",
       "8  -0.061626  0.022683  \n",
       "9   0.062666 -0.070417  \n",
       "10  0.054214 -0.211079  \n",
       "11  0.039583  0.025827  \n",
       "12 -0.075031 -0.195759  \n",
       "13 -0.676660 -0.016238  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "components=pca.components_   #14个新特征的特征向量(每一行) \n",
    "components=pd.DataFrame(components) \n",
    "components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:51.506451Z",
     "start_time": "2020-06-16T03:23:51.497454Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.25542858, 0.12862968, 0.08850255, 0.08191585, 0.07267401,\n",
       "       0.06801911, 0.05608564, 0.05187105, 0.05015169, 0.04070604,\n",
       "       0.0339251 , 0.02304823, 0.01785588, 0.01306852])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_ #主成分的解释方差百分比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:54.442229Z",
     "start_time": "2020-06-16T03:23:54.432222Z"
    }
   },
   "outputs": [],
   "source": [
    "# 重新划分训练集和测试集\n",
    "Xtrain,Xtest,Ytrain,Ytest=train_test_split(X_pca,Y, test_size=0.3, random_state=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:57.857425Z",
     "start_time": "2020-06-16T03:23:57.848431Z"
    }
   },
   "outputs": [],
   "source": [
    "# # 实例化并训练模型\n",
    "lr=LinearRegression().fit(Xtrain,Ytrain) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:59.855182Z",
     "start_time": "2020-06-16T03:23:59.839193Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7383072663535957, 0.7383072663535957)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 模型在训练集和测试机上的评分（R²）\n",
    "lr.score(Xtrain,Ytrain),lr.score(Xtrain,Ytrain)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 随机森林回归 RandomForestRegression（20分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:36:37.843504Z",
     "start_time": "2020-06-16T00:15:30.474184Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=RandomForestRegressor(bootstrap=True, ccp_alpha=0.0,\n",
       "                                             criterion='mse', max_depth=None,\n",
       "                                             max_features='auto',\n",
       "                                             max_leaf_nodes=None,\n",
       "                                             max_samples=None,\n",
       "                                             min_impurity_decrease=0.0,\n",
       "                                             min_impurity_split=None,\n",
       "                                             min_samples_leaf=1,\n",
       "                                             min_samples_split=2,\n",
       "                                             min_weight_fraction_leaf=0.0,\n",
       "                                             n_estimators=100, n_jobs=None,\n",
       "                                             oob_score=False, random_state=None,\n",
       "                                             verbose=0, warm_start=False),\n",
       "             iid='deprecated', n_jobs=1,\n",
       "             param_grid={'max_depth': range(1, 10),\n",
       "                         'n_estimators': range(1, 200, 10)},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1.使用集成算法-随机森林回归器\n",
    "# 2.使用网格搜索寻找主要参数的最优取值组合\n",
    "# 3.使用去除异常值后的训练集数据训练模型（15分）\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:39:36.195735Z",
     "start_time": "2020-06-16T00:39:36.191739Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 9, 'n_estimators': 181}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看最优参数组合\n",
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:40:27.950412Z",
     "start_time": "2020-06-16T00:40:22.666442Z"
    }
   },
   "outputs": [],
   "source": [
    "# 用上面得到的最优参数组合重新实例化模型\n",
    "rfr=RandomForestRegressor(n_estimators=181,\n",
    "                         max_depth=9,).fit(X_train,Y_train)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:40:30.380572Z",
     "start_time": "2020-06-16T00:40:30.207691Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9150260289679837, 0.8561379054489956)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr.score(X_train,Y_train),rfr.score(X_test,Y_test)   # 随机森林回归算法的score返回的是R²，并不是mse(尽管实例化时用的criterion='mse') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:40:48.947100Z",
     "start_time": "2020-06-16T00:40:39.499061Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1596168141063878"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # 使用交叉验证---随机森林回归器在测试集上的MSE平均得分（5分）\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:36:54.809803Z",
     "start_time": "2020-06-16T00:36:54.803807Z"
    },
    "scrolled": false
   },
   "source": [
    "'''\n",
    "可以看出：无论是在训练集上还是测试集上，随机森林的的表现均优于LinearRegression,Lasson和Ridge：\n",
    "训练集上的MSE由原来的0.25降到了0.15，R²由原来的0.75上升到了0.85。\n",
    "'''"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "213.988px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
